Search

Your search keyword '"Bartolucci A"' showing total 378 results
Start Over Author "Bartolucci A" Database OAIster
378 results on '"Bartolucci A"'

1. Best practices, errors, and perspectives of half a century of plant translocation in Italy

Author

D'Agostino, M, Cao Pinna, L, Carboni, M, Assini, S, Bacchetta, G, Bartolucci, F, Brancaleoni, L, Buldrini, F, Carta, A, Cerabolini, B, Ceriani, R, Clementi, U, Cogoni, D, Conti, F, Crosti, R, Cuena-Lombrana, A, De Vitis, M, Di Giustino, A, Fabrini, G, Farris, E, Fenu, G, Fiorentin, R, Foggi, B, Forte, L, Garfi, G, Gentili, R, Giusso Del Galdo, G, Martinelli, V, Medagli, P, Nonis, D, Orsenigo, S, Paoli, L, Pierce, S, Pinna, M, Rainini, F, Ravera, S, Rossi, G, Schettino, A, Schicchi, R, Troia, A, Varone, L, Zappa, E, Abeli, T, D'Agostino M., Cao Pinna L., Carboni M., Assini S., Bacchetta G., Bartolucci F., Brancaleoni L., Buldrini F., Carta A., Cerabolini B., Ceriani R. M., Clementi U., Cogoni D., Conti F., Crosti R., Cuena-Lombrana A., De Vitis M., Di Giustino A., Fabrini G., Farris E., Fenu G., Fiorentin R., Foggi B., Forte L., Garfi G., Gentili R., Giusso Del Galdo G. P., Martinelli V., Medagli P., Nonis D., Orsenigo S., Paoli L., Pierce S., Pinna M. S., Rainini F., Ravera S., Rossi G., Schettino A., Schicchi R., Troia A., Varone L., Zappa E., Abeli T., D'Agostino, M, Cao Pinna, L, Carboni, M, Assini, S, Bacchetta, G, Bartolucci, F, Brancaleoni, L, Buldrini, F, Carta, A, Cerabolini, B, Ceriani, R, Clementi, U, Cogoni, D, Conti, F, Crosti, R, Cuena-Lombrana, A, De Vitis, M, Di Giustino, A, Fabrini, G, Farris, E, Fenu, G, Fiorentin, R, Foggi, B, Forte, L, Garfi, G, Gentili, R, Giusso Del Galdo, G, Martinelli, V, Medagli, P, Nonis, D, Orsenigo, S, Paoli, L, Pierce, S, Pinna, M, Rainini, F, Ravera, S, Rossi, G, Schettino, A, Schicchi, R, Troia, A, Varone, L, Zappa, E, Abeli, T, D'Agostino M., Cao Pinna L., Carboni M., Assini S., Bacchetta G., Bartolucci F., Brancaleoni L., Buldrini F., Carta A., Cerabolini B., Ceriani R. M., Clementi U., Cogoni D., Conti F., Crosti R., Cuena-Lombrana A., De Vitis M., Di Giustino A., Fabrini G., Farris E., Fenu G., Fiorentin R., Foggi B., Forte L., Garfi G., Gentili R., Giusso Del Galdo G. P., Martinelli V., Medagli P., Nonis D., Orsenigo S., Paoli L., Pierce S., Pinna M. S., Rainini F., Ravera S., Rossi G., Schettino A., Schicchi R., Troia A., Varone L., Zappa E., and Abeli T.

Abstract

Conservation translocations are becoming common conservation practice, so there is an increasing need to understand the drivers of plant translocation performance through reviews of cases at global and regional levels. The establishment of the Italian Database of Plant Translocation (IDPlanT) provides the opportunity to review the techniques used in 186 plant translocation cases performed in the last 50 years in the heart of the Mediterranean Biodiversity Hotspot. We described techniques and information available in IDPlanT and used these data to identify drivers of translocation outcomes. We tested the effect of 15 variables on survival of translocated propagules as of the last monitoring date with binomial logistic mixed-effect models. Eleven variables significantly affected survival of transplants: life form, site protection, material source, number of source populations, propagation methods, propagule life stage, planting methods, habitat suitability assessment, site preparation, aftercare, and costs. The integration of vegetation studies in the selection of suitable planting sites significantly increased the success of translocation efforts. Although posttranslocation watering had a generally positive effect on translocation outcome, other aftercare techniques did not always increase transplant survival. Finally, we found that how funds were spent appeared to be more important than the actual amount spent. Plant translocations in Italy and in the Mediterranean area should account for the complexity of speciation, gene flow, and plant migrations that has led to local adaptations and has important implications for the choice and constitution of source material.

Published
2024

2. In-vitro studies of the NaV1.5 S805L Brugada mutation: The resting cell voltage is a critical element in determining the pathological or physiological phenotype of the current

Author

Molla, D, Frosio, A, Bertoli, G, Piantoni, C, Arici, M, Bartolucci, C, Marchese, P, Bazzini, C, Barbuti, A, Rocchetti, M, Severi, S, Bucchi, A, Baruscotti, M, Molla D., Frosio A., Bertoli G., Piantoni C., Arici M., Bartolucci C., Marchese P., Bazzini C., Barbuti A., Rocchetti M., Severi S., Bucchi A., Baruscotti M., Molla, D, Frosio, A, Bertoli, G, Piantoni, C, Arici, M, Bartolucci, C, Marchese, P, Bazzini, C, Barbuti, A, Rocchetti, M, Severi, S, Bucchi, A, Baruscotti, M, Molla D., Frosio A., Bertoli G., Piantoni C., Arici M., Bartolucci C., Marchese P., Bazzini C., Barbuti A., Rocchetti M., Severi S., Bucchi A., and Baruscotti M.

Published
2024

4. The Dynamic Clamp Technique: A Robust Toolkit for Investigating Potassium Channel Function

Author

Furini, S, Bartolucci, C, Sala, L, Bartolucci C., Sala L., Furini, S, Bartolucci, C, Sala, L, Bartolucci C., and Sala L.

Abstract

The dynamic clamp technique has emerged as a powerful tool in the field of cardiac electrophysiology, enabling researchers to investigate the intricate dynamics of ion currents in cardiac cells. Potassium channels play a critical role in the functioning of cardiac cells and the overall electrical stability of the heart. This chapter provides a comprehensive overview of the methods and applications of dynamic clamp in the study of key potassium currents in cardiac cells. A step-by-step guide is presented, detailing the experimental setup and protocols required for implementing the dynamic clamp technique in cardiac cell studies. Special attention is given to the design and construction of a dynamic clamp setup with Real Time eXperimental Interface, configurations, and the incorporation of mathematical models to mimic ion channel behavior. The chapter’s core focuses on applying dynamic clamp to elucidate the properties of various potassium channels in cardiac cells. It discusses how dynamic clamp can be used to investigate channel kinetics, voltage-dependent properties, and the impact of different potassium channel subtypes on cardiac electrophysiology. The chapter will also include examples of specific dynamic clamp experiments that studied potassium currents or their applications in cardiac cells.

Published
2024

5. Maximum Likelihood Estimation of Multivariate Regime Switching Student-t Copula Models

Author

Cortese, F, Pennoni, F, Bartolucci, F, Cortese, F., Pennoni, F., Bartolucci, F., Cortese, F, Pennoni, F, Bartolucci, F, Cortese, F., Pennoni, F., and Bartolucci, F.

Abstract

We propose a multivariate regime switching model based on a Student- (Formula presented.) copula function with parameters controlling the strength of correlation between variables and that are governed by a latent Markov process. To estimate model parameters by maximum likelihood, we consider a two-step procedure carried out through the Expectation–Maximisation algorithm. To address the main computational burden related to the estimation of the matrix of dependence parameters and the number of degrees of freedom of the Student- (Formula presented.) copula, we show a novel use of the Lagrange multipliers, which simplifies the estimation process. The simulation study shows that the estimators have good finite sample properties and the estimation procedure is computationally efficient. An application concerning log-returns of five cryptocurrencies shows that the model permits identifying bull and bear market periods based on the intensity of the correlations between crypto assets.

Published
2024

6. An analysis of the effect of streaming on civic participation through a causal hidden Markov model

Author

Bartolucci, P, Favaro, D, Pennoni, F, Sciulli, D, Bartolucci, P., Favaro, D., Pennoni, F., Sciulli, D., Bartolucci, P, Favaro, D, Pennoni, F, Sciulli, D, Bartolucci, P., Favaro, D., Pennoni, F., and Sciulli, D.

Abstract

We examine the effect of streaming based on ability levels on individuals’ civic participation throughout their adult life. The hypothesis we test is that ability grouping influences individuals’ general self-concept and, consequently, their civic participation choices across the life course. We employ data from the British National Child Development Study, which follows all UK citizens born during a certain week in 1958. Six binary variables observed at 33, 42, and 51 years of age are considered to measure civic participation. Our approach defines causal estimands with multiple treatments referring to the evolution of civic engagement over time in terms of potential versions of a sequence of latent variables assumed to follow a Markov chain with initial and transition probabilities depending on posttreatment time-varying covariates. The model also addresses partially or entirely missing data on one or more indicators at a given time occasion and missing posttreatment covariate values using dummy indicators. The model is estimated by maximizing a weighted log-likelihood function with weights corresponding to the inverse probability of the received treatment obtained from a multinomial logit model based on pretreatment covariates. Our results show that ability grouping affects the civic participation of high-ability individuals when they are 33 years old with respect to participation in general elections.

Published
2024

7. Cryptocurrency co-investment network: token returns reflect investment patterns

Author

Mungo, Luca, Bartolucci, Silvia, Alessandretti, Laura, Mungo, Luca, Bartolucci, Silvia, and Alessandretti, Laura

Abstract

Since the introduction of Bitcoin in 2009, the dramatic and unsteady evolution of the cryptocurrency market has also been driven by large investments by traditional and cryptocurrency-focused hedge funds. Notwithstanding their critical role, our understanding of the relationship between institutional investments and the evolution of the cryptocurrency market has remained limited, also due to the lack of comprehensive data describing investments over time. In this study, we present a quantitative study of cryptocurrency institutional investments based on a dataset collected for 1324 currencies in the period between 2014 and 2022 from Crunchbase, one of the largest platforms gathering business information. We show that the evolution of the cryptocurrency market capitalization is highly correlated with the size of institutional investments, thus confirming their important role. Further, we find that the market is dominated by the presence of a group of prominent investors who tend to specialise by focusing on particular technologies. Finally, studying the co-investment network of currencies that share common investors, we show that assets with shared investors tend to be characterized by similar market behaviour. Our work sheds light on the role played by institutional investors and provides a basis for further research on their influence in the cryptocurrency ecosystem.

Published
2024

8. On the connection between uniqueness from samples and stability in Gabor phase retrieval

Author

Alaifari, Rima (author), Bartolucci, F. (author), Steinerberger, Stefan (author), Wellershoff, Matthias (author), Alaifari, Rima (author), Bartolucci, F. (author), Steinerberger, Stefan (author), and Wellershoff, Matthias (author)

Abstract

Gabor phase retrieval is the problem of reconstructing a signal from only the magnitudes of its Gabor transform. Previous findings suggest a possible link between unique solvability of the discrete problem (recovery from measurements on a lattice) and stability of the continuous problem (recovery from measurements on an open subset of R2). In this paper, we close this gap by proving that such a link cannot be made. More precisely, we establish the existence of functions which break uniqueness from samples without affecting stability of the continuous problem. Furthermore, we prove the novel result that counterexamples to unique recovery from samples are dense in L2(R) . Finally, we develop an intuitive argument on the connection between directions of instability in phase retrieval and certain Laplacian eigenfunctions associated to small eigenvalues., Analysis

Published
2024
Full Text
View/download PDF

9. Maximum likelihood for discrete latent variable models via evolutionary algorithms

Author

Brusa, L, Pennoni, F, Bartolucci, F, Brusa, L, Pennoni, F, and Bartolucci, F

Abstract

We propose an evolutionary optimization method for maximum likelihood and approximate maximum likelihood estimation of discrete latent variable models. The proposal is based on modified versions of the expectation–maximization (EM) and variational EM (VEM) algorithms, which are based on the genetic approach and allow us to accurately explore the parameter space, reducing the chance to be trapped into one of the multiple local maxima of the log-likelihood function. Their performance is examined through an extensive Monte Carlo simulation study where they are employed to estimate latent class, hidden Markov, and stochastic block models and compared with the standard EM and VEM algorithms. We observe a significant increase in the chance to reach global maximum of the target function and a high accuracy of the estimated parameters for each model. Applications focused on the analysis of cross-sectional, longitudinal, and network data are proposed to illustrate and compare the algorithms.

Published
2024

10. Variable selection for hidden Markov models with continuous variables and missing data

Author

Pennoni, F, Bartolucci, F, Pandolfi, S, Pennoni, F, Bartolucci, F, and Pandolfi, S

Abstract

We propose a variable selection method for multivariate hidden Markov models with continuous responses that are partially or completely missing at a given time occasion. Through this procedure, we achieve a dimensionality reduction by selecting the subset of the most informative responses for clustering individuals and simultaneously choosing the optimal number of these clusters corresponding to latent states. The approach is based on comparing different model specifications in terms of the subset of responses assumed to be dependent on the latent states, and it relies on a greedy search algorithm based on the Bayesian information criterion seen as an approximation of the Bayes factor. A suitable expectation-maximization algorithm is employed to obtain maximum likelihood estimates of the model parameters under the missing-at-random assumption. The proposal is illustrated via Monte Carlo simulation and an application where development indicators collected over eighteen years are selected, and countries are clustered into groups to evaluate their growth over time.

Published
2024

12. Deep Limit Order Book Forecasting

Author

Briola, Antonio, Bartolucci, Silvia, Aste, Tomaso, Briola, Antonio, Bartolucci, Silvia, and Aste, Tomaso

Abstract

We exploit cutting-edge deep learning methodologies to explore the predictability of high-frequency Limit Order Book mid-price changes for a heterogeneous set of stocks traded on the NASDAQ exchange. In so doing, we release `LOBFrame', an open-source code base to efficiently process large-scale Limit Order Book data and quantitatively assess state-of-the-art deep learning models' forecasting capabilities. Our results are twofold. We demonstrate that the stocks' microstructural characteristics influence the efficacy of deep learning methods and that their high forecasting power does not necessarily correspond to actionable trading signals. We argue that traditional machine learning metrics fail to adequately assess the quality of forecasts in the Limit Order Book context. As an alternative, we propose an innovative operational framework that evaluates predictions' practicality by focusing on the probability of accurately forecasting complete transactions. This work offers academics and practitioners an avenue to make informed and robust decisions on the application of deep learning techniques, their scope and limitations, effectively exploiting emergent statistical properties of the Limit Order Book., Comment: 43 pages, 14 figures, 12 Tables

Published
2024

13. Neural reproducing kernel Banach spaces and representer theorems for deep networks

Author

Bartolucci, Francesca, De Vito, Ernesto, Rosasco, Lorenzo, Vigogna, Stefano, Bartolucci, Francesca, De Vito, Ernesto, Rosasco, Lorenzo, and Vigogna, Stefano

Abstract

Studying the function spaces defined by neural networks helps to understand the corresponding learning models and their inductive bias. While in some limits neural networks correspond to function spaces that are reproducing kernel Hilbert spaces, these regimes do not capture the properties of the networks used in practice. In contrast, in this paper we show that deep neural networks define suitable reproducing kernel Banach spaces. These spaces are equipped with norms that enforce a form of sparsity, enabling them to adapt to potential latent structures within the input data and their representations. In particular, leveraging the theory of reproducing kernel Banach spaces, combined with variational results, we derive representer theorems that justify the finite architectures commonly employed in applications. Our study extends analogous results for shallow networks and can be seen as a step towards considering more practically plausible neural architectures.

Published
2024

14. DApps Ecosystems: Mapping the Network Structure of Smart Contract Interactions

Author

Aufiero, Sabrina, Ibba, Giacomo, Bartolucci, Silvia, Destefanis, Giuseppe, Neykova, Rumyana, Ortu, Marco, Aufiero, Sabrina, Ibba, Giacomo, Bartolucci, Silvia, Destefanis, Giuseppe, Neykova, Rumyana, and Ortu, Marco

Abstract

In recent years, decentralized applications (dApps) built on blockchain platforms such as Ethereum and coded in languages such as Solidity, have gained attention for their potential to disrupt traditional centralized systems. Despite their rapid adoption, limited research has been conducted to understand the underlying code structure of these applications. In particular, each dApp is composed of multiple smart contracts, each containing a number of functions that can be called to trigger a specific event, e.g., a token transfer. In this paper, we reconstruct and analyse the network of contracts and functions calls within the dApp, which is helpful to unveil vulnerabilities that can be exploited by malicious attackers. We show how decentralization is architecturally implemented, identifying common development patterns and anomalies that could influence the system's robustness and efficiency. We find a consistent network structure characterized by modular, self-sufficient contracts and a complex web of function interactions, indicating common coding practices across the blockchain community. Critically, a small number of key functions within each dApp play a pivotal role in maintaining network connectivity, making them potential targets for cyber attacks and highlighting the need for robust security measures., Comment: 28 pages, 23 figures

Published
2024

15. HLOB -- Information Persistence and Structure in Limit Order Books

Author

Briola, Antonio, Bartolucci, Silvia, Aste, Tomaso, Briola, Antonio, Bartolucci, Silvia, and Aste, Tomaso

Abstract

We introduce a novel large-scale deep learning model for Limit Order Book mid-price changes forecasting, and we name it `HLOB'. This architecture (i) exploits the information encoded by an Information Filtering Network, namely the Triangulated Maximally Filtered Graph, to unveil deeper and non-trivial dependency structures among volume levels; and (ii) guarantees deterministic design choices to handle the complexity of the underlying system by drawing inspiration from the groundbreaking class of Homological Convolutional Neural Networks. We test our model against 9 state-of-the-art deep learning alternatives on 3 real-world Limit Order Book datasets, each including 15 stocks traded on the NASDAQ exchange, and we systematically characterize the scenarios where HLOB outperforms state-of-the-art architectures. Our approach sheds new light on the spatial distribution of information in Limit Order Books and on its degradation over increasing prediction horizons, narrowing the gap between microstructural modeling and deep learning-based forecasting in high-frequency financial markets., Comment: 34 pages, 7 figures, 7 tables, 3 equations

Published
2024

16. Maximum likelihood inference for hidden Markov models with parsimonious parametrizations of transition matrices

Author

Pandolfi, S, Bartolucci, F, Pennoni, F, Pandolfi, S., Bartolucci, F., Pennoni, F., Pandolfi, S, Bartolucci, F, Pennoni, F, Pandolfi, S., Bartolucci, F., and Pennoni, F.

Abstract

In longitudinal data analysis, hidden Markov (HM) models are fundamental tools, especially when the analysis is focused on transitions or the need to cluster individuals dynamically. When individual covariates are available in the dataset, a typical problem is how to parametrize the transition probabilities based on these covariates in a parsimonious way. In fact, standard multinomial parametrizations of these probabilities lead to models with many parameters, which are also difficult to interpret and, consequently, to unstable parameter estimates. To overcome the above problems, different parametrizations of the transition probabilities of HM models with covariates are introduced based on multinomial logit models formulated by two different choices of the reference state of each logit. These parametrizations rely on constraints having a straightforward interpretation, making the model much more parsimonious. Estimation based on the maximum likelihood (ML) approach is developed under different constraints based on the Expectation-Maximization algorithm. Steps of Newton-Raphson type are also included to improve the algorithm’s convergence speed

Published
2023

17. A causal hidden Markov model for assessing effects of multiple direct mail campaigns

Author

Pennoni, F, Paas, L, Bartolucci, F, Pennoni, F., Paas, L. J., Bartolucci, F., Pennoni, F, Paas, L, Bartolucci, F, Pennoni, F., Paas, L. J., and Bartolucci, F.

Abstract

We propose assessing the causal effects of a dynamic treatment in a longitudinal observational study, given observed confounders under suitable assumptions. The causal hidden Markov model is based on potential versions of discrete latent variables, and it accounts for the estimated propensity to be assigned to each treatment level over time using inverse probability weighting. Estimation of the model parameters is carried out through a weighted maximum log-likelihood approach. Standard errors for the parameter estimates are provided by nonparametric bootstrap. The proposal is validated through a simulation study aimed at comparing different model specifications. As an illustrative example, we consider a marketing campaign conducted by a large European bank over time on its customers. Findings provide straightforward managerial implications.

Published
2023

18. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Author

Sherratt, K, Gruson, H, Grah, R, Johnson, H, Niehus, R, Prasse, B, Sandmann, F, Deuschel, J, Wolffram, D, Abbott, S, Ullrich, A, Gibson, G, L Ray, E, G Reich, N, Sheldon, D, Wang, Y, Wattanachit, N, Wang, L, Trnka, J, Obozinski, G, Sun, T, Thanou, D, Pottier, L, Krymova, E, H Meinke, J, Vittoria Barbarossa, M, Leithäuser, N, Mohring, J, Schneider, J, Włazło, J, Fuhrmann, J, Lange, B, Rodiah, I, Baccam, P, Gurung, H, Stage, S, Suchoski, B, Budzinski, J, Walraven, R, Villanueva, I, Tucek, V, Smid, M, Zajíček, M, Pérez Álvarez, C, Reina, B, I Bosse, N, R Meakin, S, Castro, L, Fairchild, G, Michaud, I, Osthus, D, Alaimo Di Loro, P, Maruotti, A, Eclerová, V, Kraus, A, Kraus, D, Pribylova, L, Dimitris, B, Lingzhi Li, M, Saksham, S, Dehning, J, Mohr, S, Priesemann, V, Redlarski, G, Bejar, B, Ardenghi, G, Parolini, N, Ziarelli, G, Bock, W, Heyder, S, Hotz, T, E Singh, D, Guzman-Merino, M, L Aznarte, J, Moriña, D, Alonso, S, Álvarez, E, López, D, Prats, C, Pablo Burgard, J, Rodloff, A, Zimmermann, T, Kuhlmann, A, Zibert, J, Pennoni, F, Divino, F, Català, M, Lovison, G, Giudici, P, Tarantino, B, Bartolucci, F, Jona Lasinio, G, Mingione, M, Farcomeni, A, Srivastava, A, Montero-Manso, P, Adiga, A, Hurt, B, Lewis, B, Marathe, M, Porebski, P, Venkatramanan, S, P Bartczuk, R, Dreger, F, Gambin, A, Gogolewski, K, Gruziel-Słomka, M, Krupa, B, Moszyński, A, Niedzielewski, K, Nowosielski, J, Radwan, M, Rakowski, F, Semeniuk, M, Szczurek, E, Zieliński, J, Kisielewski, J, Pabjan, B, Kirsten, H, Kheifetz, Y, Scholz, M, Biecek, P, Bodych, M, Filinski, M, Idzikowski, R, Krueger, T, Ozanski, T, Bracher, J, Funk, S, Katharine Sherratt, Hugo Gruson, Rok Grah, Helen Johnson, Rene Niehus, Bastian Prasse, Frank Sandmann, Jannik Deuschel, Daniel Wolffram, Sam Abbott, Alexander Ullrich, Graham Gibson, Evan L Ray, Nicholas G Reich, Daniel Sheldon, Yijin Wang, Nutcha Wattanachit, Lijing Wang, Jan Trnka, Guillaume Obozinski, Tao Sun, Dorina Thanou, Loic Pottier, Ekaterina Krymova, Jan H Meinke, Maria Vittoria Barbarossa, Neele Leithäuser, Jan Mohring, Johanna Schneider, Jaroslaw Włazło, Jan Fuhrmann, Berit Lange, Isti Rodiah, Prasith Baccam, Heidi Gurung, Steven Stage, Bradley Suchoski, Jozef Budzinski, Robert Walraven, Inmaculada Villanueva, Vit Tucek, Martin Smid, Milan Zajíček, Cesar Pérez Álvarez, Borja Reina, Nikos I Bosse, Sophie R Meakin, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Pierfrancesco Alaimo Di Loro, Antonello Maruotti, Veronika Eclerová, Andrea Kraus, David Kraus, Lenka Pribylova, Bertsimas Dimitris, Michael Lingzhi Li, Soni Saksham, Jonas Dehning, Sebastian Mohr, Viola Priesemann, Grzegorz Redlarski, Benjamin Bejar, Giovanni Ardenghi, Nicola Parolini, Giovanni Ziarelli, Wolfgang Bock, Stefan Heyder, Thomas Hotz, David E Singh, Miguel Guzman-Merino, Jose L Aznarte, David Moriña, Sergio Alonso, Enric Álvarez, Daniel López, Clara Prats, Jan Pablo Burgard, Arne Rodloff, Tom Zimmermann, Alexander Kuhlmann, Janez Zibert, Fulvia Pennoni, Fabio Divino, Marti Català, Gianfranco Lovison, Paolo Giudici, Barbara Tarantino, Francesco Bartolucci, Giovanna Jona Lasinio, Marco Mingione, Alessio Farcomeni, Ajitesh Srivastava, Pablo Montero-Manso, Aniruddha Adiga, Benjamin Hurt, Bryan Lewis, Madhav Marathe, Przemyslaw Porebski, Srinivasan Venkatramanan, Rafal P Bartczuk, Filip Dreger, Anna Gambin, Krzysztof Gogolewski, Magdalena Gruziel-Słomka, Bartosz Krupa, Antoni Moszyński, Karol Niedzielewski, Jedrzej Nowosielski, Maciej Radwan, Franciszek Rakowski, Marcin Semeniuk, Ewa Szczurek, Jakub Zieliński, Jan Kisielewski, Barbara Pabjan, Holger Kirsten, Yuri Kheifetz, Markus Scholz, Przemyslaw Biecek, Marcin Bodych, Maciej Filinski, Radoslaw Idzikowski, Tyll Krueger, Tomasz Ozanski, Johannes Bracher, Sebastian Funk, Sherratt, K, Gruson, H, Grah, R, Johnson, H, Niehus, R, Prasse, B, Sandmann, F, Deuschel, J, Wolffram, D, Abbott, S, Ullrich, A, Gibson, G, L Ray, E, G Reich, N, Sheldon, D, Wang, Y, Wattanachit, N, Wang, L, Trnka, J, Obozinski, G, Sun, T, Thanou, D, Pottier, L, Krymova, E, H Meinke, J, Vittoria Barbarossa, M, Leithäuser, N, Mohring, J, Schneider, J, Włazło, J, Fuhrmann, J, Lange, B, Rodiah, I, Baccam, P, Gurung, H, Stage, S, Suchoski, B, Budzinski, J, Walraven, R, Villanueva, I, Tucek, V, Smid, M, Zajíček, M, Pérez Álvarez, C, Reina, B, I Bosse, N, R Meakin, S, Castro, L, Fairchild, G, Michaud, I, Osthus, D, Alaimo Di Loro, P, Maruotti, A, Eclerová, V, Kraus, A, Kraus, D, Pribylova, L, Dimitris, B, Lingzhi Li, M, Saksham, S, Dehning, J, Mohr, S, Priesemann, V, Redlarski, G, Bejar, B, Ardenghi, G, Parolini, N, Ziarelli, G, Bock, W, Heyder, S, Hotz, T, E Singh, D, Guzman-Merino, M, L Aznarte, J, Moriña, D, Alonso, S, Álvarez, E, López, D, Prats, C, Pablo Burgard, J, Rodloff, A, Zimmermann, T, Kuhlmann, A, Zibert, J, Pennoni, F, Divino, F, Català, M, Lovison, G, Giudici, P, Tarantino, B, Bartolucci, F, Jona Lasinio, G, Mingione, M, Farcomeni, A, Srivastava, A, Montero-Manso, P, Adiga, A, Hurt, B, Lewis, B, Marathe, M, Porebski, P, Venkatramanan, S, P Bartczuk, R, Dreger, F, Gambin, A, Gogolewski, K, Gruziel-Słomka, M, Krupa, B, Moszyński, A, Niedzielewski, K, Nowosielski, J, Radwan, M, Rakowski, F, Semeniuk, M, Szczurek, E, Zieliński, J, Kisielewski, J, Pabjan, B, Kirsten, H, Kheifetz, Y, Scholz, M, Biecek, P, Bodych, M, Filinski, M, Idzikowski, R, Krueger, T, Ozanski, T, Bracher, J, Funk, S, Katharine Sherratt, Hugo Gruson, Rok Grah, Helen Johnson, Rene Niehus, Bastian Prasse, Frank Sandmann, Jannik Deuschel, Daniel Wolffram, Sam Abbott, Alexander Ullrich, Graham Gibson, Evan L Ray, Nicholas G Reich, Daniel Sheldon, Yijin Wang, Nutcha Wattanachit, Lijing Wang, Jan Trnka, Guillaume Obozinski, Tao Sun, Dorina Thanou, Loic Pottier, Ekaterina Krymova, Jan H Meinke, Maria Vittoria Barbarossa, Neele Leithäuser, Jan Mohring, Johanna Schneider, Jaroslaw Włazło, Jan Fuhrmann, Berit Lange, Isti Rodiah, Prasith Baccam, Heidi Gurung, Steven Stage, Bradley Suchoski, Jozef Budzinski, Robert Walraven, Inmaculada Villanueva, Vit Tucek, Martin Smid, Milan Zajíček, Cesar Pérez Álvarez, Borja Reina, Nikos I Bosse, Sophie R Meakin, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Pierfrancesco Alaimo Di Loro, Antonello Maruotti, Veronika Eclerová, Andrea Kraus, David Kraus, Lenka Pribylova, Bertsimas Dimitris, Michael Lingzhi Li, Soni Saksham, Jonas Dehning, Sebastian Mohr, Viola Priesemann, Grzegorz Redlarski, Benjamin Bejar, Giovanni Ardenghi, Nicola Parolini, Giovanni Ziarelli, Wolfgang Bock, Stefan Heyder, Thomas Hotz, David E Singh, Miguel Guzman-Merino, Jose L Aznarte, David Moriña, Sergio Alonso, Enric Álvarez, Daniel López, Clara Prats, Jan Pablo Burgard, Arne Rodloff, Tom Zimmermann, Alexander Kuhlmann, Janez Zibert, Fulvia Pennoni, Fabio Divino, Marti Català, Gianfranco Lovison, Paolo Giudici, Barbara Tarantino, Francesco Bartolucci, Giovanna Jona Lasinio, Marco Mingione, Alessio Farcomeni, Ajitesh Srivastava, Pablo Montero-Manso, Aniruddha Adiga, Benjamin Hurt, Bryan Lewis, Madhav Marathe, Przemyslaw Porebski, Srinivasan Venkatramanan, Rafal P Bartczuk, Filip Dreger, Anna Gambin, Krzysztof Gogolewski, Magdalena Gruziel-Słomka, Bartosz Krupa, Antoni Moszyński, Karol Niedzielewski, Jedrzej Nowosielski, Maciej Radwan, Franciszek Rakowski, Marcin Semeniuk, Ewa Szczurek, Jakub Zieliński, Jan Kisielewski, Barbara Pabjan, Holger Kirsten, Yuri Kheifetz, Markus Scholz, Przemyslaw Biecek, Marcin Bodych, Maciej Filinski, Radoslaw Idzikowski, Tyll Krueger, Tomasz Ozanski, Johannes Bracher, and Sebastian Funk

Abstract

Background: Short-term forecasts of infectious disease contribute to situational awareness and capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise forecasts’ predictive performance by combining independent models into an ensemble. Here we report the performance of ensemble predictions of COVID-19 cases and deaths across Europe from March 2021 to March 2022. Methods: We created the European COVID-19 Forecast Hub, an online open-access platform where modellers upload weekly forecasts for 32 countries with results publicly visualised and evaluated. We created a weekly ensemble forecast from the equally-weighted average across individual models’ predictive quantiles. We measured forecast accuracy using a baseline and relative Weighted Interval Score (rWIS). We retrospectively explored ensemble methods, including weighting by past performance. Results: We collected weekly forecasts from 48 models, of which we evaluated 29 models alongside the ensemble model. The ensemble had a consistently strong performance across countries over time, performing better on rWIS than 91% of forecasts for deaths (N=763 predictions from 20 models), and 83% forecasts for cases (N=886 predictions from 23 models). Performance remained stable over a 4-week horizon for death forecasts but declined with longer horizons for cases. Among ensemble methods, the most influential choice came from using a median average instead of the mean, regardless of weighting component models. Conclusions: Our results support combining independent models into an ensemble forecast to improve epidemiological predictions, and suggest that median averages yield better performance than methods based on means. We highlight that forecast consumers should place more weight on incident death forecasts than case forecasts at horizons greater than two weeks. Funding: European Commission, Ministerio de Ciencia, Innovación y Universidades, FEDER; Ag

Published
2023

19. A Causal Latent Transition Model With Multivariate Outcomes and Unobserved Heterogeneity: Application to Human Capital Development

Author

Bartolucci, F, Pennoni, F, Vittadini, G, Bartolucci, Francesco, Pennoni, Fulvia, Vittadini, Giorgio, Bartolucci, F, Pennoni, F, Vittadini, G, Bartolucci, Francesco, Pennoni, Fulvia, and Vittadini, Giorgio

Abstract

In order to evaluate the effect of a policy or treatment with pre- and post-treatment outcomes, we propose an approach based on a transition model, which may be applied with multivariate outcomes and accounts for unobserved heterogeneity. This model is based on potential versions of discrete latent variables representing the individual characteristic of interest and may be cast in the hidden (latent) Markov literature for panel data. Therefore, it can be estimated by maximum likelihood in a relatively simple way. The approach extends the difference-in-difference method as it is possible to deal with multivariate outcomes. Moreover, causal effects may be expressed with respect to transition probabilities. The proposal is validated through a simulation study, and it is applied to evaluate educational programs administered to pupils in the sixth and seventh grades during their middle school period. These programs are carried out in an Italian region to improve non-cognitive skills (CSs). We study if they impact also on students’ CSs in Italian and Mathematics in the eighth grade, exploiting the pretreatment test scores available in the fifth grade. The main conclusion is that the educational programs aimed to develop noncognitive abilities help the best students to maintain their higher cognitive abilities over time.

Published
2023

20. A dynamic clamping approach using in silico IK1 current for discrimination of chamber-specific hiPSC-derived cardiomyocytes

Author

Altomare, C, Bartolucci, C, Sala, L, Balbi, C, Burrello, J, Pietrogiovanna, N, Burrello, A, Bolis, S, Panella, S, Arici, M, Krause, R, Rocchetti, M, Severi, S, Barile, L, Altomare, Claudia, Bartolucci, Chiara, Sala, Luca, Balbi, Carolina, Burrello, Jacopo, Pietrogiovanna, Nicole, Burrello, Alessio, Bolis, Sara, Panella, Stefano, Arici, Martina, Krause, Rolf, Rocchetti, Marcella, Severi, Stefano, Barile, Lucio, Altomare, C, Bartolucci, C, Sala, L, Balbi, C, Burrello, J, Pietrogiovanna, N, Burrello, A, Bolis, S, Panella, S, Arici, M, Krause, R, Rocchetti, M, Severi, S, Barile, L, Altomare, Claudia, Bartolucci, Chiara, Sala, Luca, Balbi, Carolina, Burrello, Jacopo, Pietrogiovanna, Nicole, Burrello, Alessio, Bolis, Sara, Panella, Stefano, Arici, Martina, Krause, Rolf, Rocchetti, Marcella, Severi, Stefano, and Barile, Lucio

Abstract

Human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes (CM) constitute a mixed population of ventricular-, atrial-, nodal-like cells, limiting the reliability for studying chamber-specific disease mechanisms. Previous studies characterised CM phenotype based on action potential (AP) morphology, but the classification criteria were still undefined. Our aim was to use in silico models to develop an automated approach for discriminating the electrophysiological differences between hiPSC-CM. We propose the dynamic clamp (DC) technique with the injection of a specific IK1 current as a tool for deriving nine electrical biomarkers and blindly classifying differentiated CM. An unsupervised learning algorithm was applied to discriminate CM phenotypes and principal component analysis was used to visualise cell clustering. Pharmacological validation was performed by specific ion channel blocker and receptor agonist. The proposed approach improves the translational relevance of the hiPSC-CM model for studying mechanisms underlying inherited or acquired atrial arrhythmias in human CM, and for screening anti-arrhythmic agents.

Published
2023

21. Tempered expectation-maximization algorithm for the estimation of discrete latent variable models

Author

Brusa, L, Bartolucci, F, Pennoni, F, Brusa Luca, Bartolucci Francesco, Pennoni Fulvia, Brusa, L, Bartolucci, F, Pennoni, F, Brusa Luca, Bartolucci Francesco, and Pennoni Fulvia

Abstract

Maximum likelihood estimation of discrete latent variable (DLV) models is usually performed by the expectation-maximization (EM) algorithm. A well-known drawback is related to the multimodality of the log-likelihood function so that the estimation algorithm can converge to a local maximum, not corresponding to the global one. We propose a tempered EM algorithm to explore the parameter space adequately for two main classes of DLV models, namely latent class and hidden Markov. We compare the proposal with the standard EM algorithm by an extensive Monte Carlo simulation study, evaluating both the ability to reach the global maximum and the computational time. We show the results of the analysis of discrete and continuous cross-sectional and longitudinal data referring to some applications of interest. All the results provide supporting evidence that the proposal outperforms the standard EM algorithm, and it significantly improves the chance to reach the global maximum. The advantage is relevant even considering the overall computing time.

Published
2023

22. Immune-Checkpoint-Inhibitor-Related Lung Toxicity: A Multicentre Real-Life Retrospective Portrait from Six Italian Centres

Author

Cameli, P, Faverio, P, Ferrari, K, Bonti, V, Marsili, S, Mazzei, M, Mazzoni, F, Bartolucci, M, Scotti, V, Bertolini, F, Barbieri, F, Baldessari, C, Veronese, C, Boffi, R, Brighenti, M, Cortinovis, D, Dominici, M, Pesci, A, Bargagli, E, Luppi, F, Cameli P., Faverio P., Ferrari K., Bonti V., Marsili S., Mazzei M. A., Mazzoni F., Bartolucci M., Scotti V., Bertolini F., Barbieri F., Baldessari C., Veronese C., Boffi R., Brighenti M., Cortinovis D., Dominici M., Pesci A., Bargagli E., Luppi F., Cameli, P, Faverio, P, Ferrari, K, Bonti, V, Marsili, S, Mazzei, M, Mazzoni, F, Bartolucci, M, Scotti, V, Bertolini, F, Barbieri, F, Baldessari, C, Veronese, C, Boffi, R, Brighenti, M, Cortinovis, D, Dominici, M, Pesci, A, Bargagli, E, Luppi, F, Cameli P., Faverio P., Ferrari K., Bonti V., Marsili S., Mazzei M. A., Mazzoni F., Bartolucci M., Scotti V., Bertolini F., Barbieri F., Baldessari C., Veronese C., Boffi R., Brighenti M., Cortinovis D., Dominici M., Pesci A., Bargagli E., and Luppi F.

Abstract

Background: Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic horizons of various cancers. However, immune-related adverse events have been reported, including interstitial lung diseases. Our aim was to describe the clinical and radiological features and survival of a multicentre cohort of patients who developed ICI-related lung toxicity. Methods: Six Italian centres were involved in the study. Patients who were treated with anti-PD-1/PD-L1 and CTLA-4 mAbs and developed ICI-related lung toxicity were recruited retrospectively to study clinical, radiological, immunological and survival data. Results: A total of 41 patients (25 males, 66.8 ± 9.9 years) were enrolled. Lung toxicity occurred after 204.3 ± 208.3 days of therapy, with ground glass opacities being the most common HRCT pattern (23 cases). Male sex, lung cancer and acute respiratory failure were associated with a shorter latency of toxicity (p = 0.0030, p = 0.0245 and p = 0.0390, respectively). Patients who required high-flow oxygen therapy showed significantly worse survival (p = 0.0028). Conclusions: Our cohort showed heterogeneous clinical and radiological aspects of ICI-related lung toxicity, with a latency not limited to the first year of treatment. Severity was mainly mild to moderate, although life-threatening events did occur. Our data indicate that strict long-term follow-up is needed to enable early diagnosis and appropriate management.

Published
2022

23. Discrete Latent Variable Models

Author

Bartolucci, F, Pandolfi, S, Pennoni, F, Bartolucci, Francesco, Pandolfi, Silvia, Pennoni, Fulvia, Bartolucci, F, Pandolfi, S, Pennoni, F, Bartolucci, Francesco, Pandolfi, Silvia, and Pennoni, Fulvia

Abstract

We review the discrete latent variable approach, which is very popular in statistics and related fields. It allows us to formulate interpretable and flexible models that can be used to analyze complex datasets in the presence of articulated dependence structures among variables. Specific models including discrete latent variables are illustrated, such as finite mixture, latent class, hidden Markov, and stochastic block models. Algorithms for maximum likelihood and Bayesian estimation of these models are reviewed, focusing, in particular, on the expectation-maximization algorithm and the Markov chain Monte Carlo method with data augmentation. Model selection, particularly concerning the number of support points of the latent distribution, is discussed. The approach is illustrated by summarizing applications available in the literature; a brief review of the main software packages to handle discrete latent variable models is also provided. Finally, some possible developments in this literature are suggested.

Published
2022

24. Notulae to the Italian alien vascular flora: 14

Author

Galasso, G, Domina, G, Andreatta, S, Argenti, C, Astuti, G, Bacaro, G, Bacchetta, G, Bagella, S, Banfi, E, Barberis, D, Bartolucci, F, Bernardo, L, Bonari, G, Brundu, G, Buccomino, G, Calvia, G, Cancellieri, L, Capuano, A, Celesti-Grapow, L, Conti, F, Cuena-Lombraña, A, D’Amico, F, De Fine, G, de Simone, L, Guacchio, E, Emili, F, Fanfarillo, E, Fascetti, S, Fiaschi, T, Fois, M, Fortini, P, Gentili, R, Giardini, M, Hussain, A, Iamonico, D, Laface, V, Lallai, A, Lazzaro, L, Lecis, A, Ligato, E, Loi, G, Lonati, M, Lozano, V, Maccherini, S, Mainetti, A, Mascia, F, Mei, G, Menini, F, Merli, M, Montesano, A, Mugnai, M, Musarella, C, Nota, G, Olivieri, N, Passalacqua, N, Pinzani, L, Pisano, A, Pittarello, M, Podda, L, Posillipo, G, Potenza, G, Probo, M, Prosser, F, Quaglini, L, Enri, S, Rivieccio, G, Roma-Marzio, F, Rosati, L, Selvaggi, A, Soldano, A, Stinca, A, Tasinazzo, S, Tassone, S, Terzi, M, Vallariello, R, Vangelisti, R, Verloove, F, Lastrucci, L, Galasso G., Domina G., Andreatta S., Argenti C., Astuti G., Bacaro G., Bacchetta G., Bagella S., Banfi E., Barberis D., Bartolucci F., Bernardo L., Bonari G., Brundu G., Buccomino G., Calvia G., Cancellieri L., Capuano A., Celesti-Grapow L., Conti F., Cuena-Lombraña A., D’Amico F. S., De Fine G., de Simone L., Guacchio E. D., Emili F., Fanfarillo E., Fascetti S., Fiaschi T., Fois M., Fortini P., Gentili R., Giardini M., Hussain A. N., Iamonico D., Laface V. L. A., Lallai A., Lazzaro L., Lecis A. P., Ligato E., Loi G., Lonati M., Lozano V., Maccherini S., Mainetti A., Mascia F., Mei G., Menini F., Merli M., Montesano A., Mugnai M., Musarella C. M., Nota G., Olivieri N., Passalacqua N. G., Pinzani L., Pisano A., Pittarello M., Podda L., Posillipo G., Potenza G., Probo M., Prosser F., Quaglini L. A., Enri S. R., Rivieccio G., Roma-Marzio F., Rosati L., Selvaggi A., Soldano A., Stinca A., Tasinazzo S., Tassone S., Terzi M., Vallariello R., Vangelisti R., Verloove F., Lastrucci L., Galasso, G, Domina, G, Andreatta, S, Argenti, C, Astuti, G, Bacaro, G, Bacchetta, G, Bagella, S, Banfi, E, Barberis, D, Bartolucci, F, Bernardo, L, Bonari, G, Brundu, G, Buccomino, G, Calvia, G, Cancellieri, L, Capuano, A, Celesti-Grapow, L, Conti, F, Cuena-Lombraña, A, D’Amico, F, De Fine, G, de Simone, L, Guacchio, E, Emili, F, Fanfarillo, E, Fascetti, S, Fiaschi, T, Fois, M, Fortini, P, Gentili, R, Giardini, M, Hussain, A, Iamonico, D, Laface, V, Lallai, A, Lazzaro, L, Lecis, A, Ligato, E, Loi, G, Lonati, M, Lozano, V, Maccherini, S, Mainetti, A, Mascia, F, Mei, G, Menini, F, Merli, M, Montesano, A, Mugnai, M, Musarella, C, Nota, G, Olivieri, N, Passalacqua, N, Pinzani, L, Pisano, A, Pittarello, M, Podda, L, Posillipo, G, Potenza, G, Probo, M, Prosser, F, Quaglini, L, Enri, S, Rivieccio, G, Roma-Marzio, F, Rosati, L, Selvaggi, A, Soldano, A, Stinca, A, Tasinazzo, S, Tassone, S, Terzi, M, Vallariello, R, Vangelisti, R, Verloove, F, Lastrucci, L, Galasso G., Domina G., Andreatta S., Argenti C., Astuti G., Bacaro G., Bacchetta G., Bagella S., Banfi E., Barberis D., Bartolucci F., Bernardo L., Bonari G., Brundu G., Buccomino G., Calvia G., Cancellieri L., Capuano A., Celesti-Grapow L., Conti F., Cuena-Lombraña A., D’Amico F. S., De Fine G., de Simone L., Guacchio E. D., Emili F., Fanfarillo E., Fascetti S., Fiaschi T., Fois M., Fortini P., Gentili R., Giardini M., Hussain A. N., Iamonico D., Laface V. L. A., Lallai A., Lazzaro L., Lecis A. P., Ligato E., Loi G., Lonati M., Lozano V., Maccherini S., Mainetti A., Mascia F., Mei G., Menini F., Merli M., Montesano A., Mugnai M., Musarella C. M., Nota G., Olivieri N., Passalacqua N. G., Pinzani L., Pisano A., Pittarello M., Podda L., Posillipo G., Potenza G., Probo M., Prosser F., Quaglini L. A., Enri S. R., Rivieccio G., Roma-Marzio F., Rosati L., Selvaggi A., Soldano A., Stinca A., Tasinazzo S., Tassone S., Terzi M., Vallariello R., Vangelisti R., Verloove F., and Lastrucci L.

Abstract

In this contribution, new data concerning the distribution of vascular flora alien to Italy are presented. It includes new records, confirmations, and status changes for Italy or for Italian administrative regions. Nomenclatural and distribution updates, published elsewhere, and corrections are provided as Suppl. material 1. © Gabriele Galasso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published
2022

25. Immune-Checkpoint-Inhibitor-Related Lung Toxicity: A Multicentre Real-Life Retrospective Portrait from Six Italian Centres

Author

Cameli, P, Faverio, P, Ferrari, K, Bonti, V, Marsili, S, Mazzei, M, Mazzoni, F, Bartolucci, M, Scotti, V, Bertolini, F, Barbieri, F, Baldessari, C, Veronese, C, Boffi, R, Brighenti, M, Cortinovis, D, Dominici, M, Pesci, A, Bargagli, E, Luppi, F, Cameli P., Faverio P., Ferrari K., Bonti V., Marsili S., Mazzei M. A., Mazzoni F., Bartolucci M., Scotti V., Bertolini F., Barbieri F., Baldessari C., Veronese C., Boffi R., Brighenti M., Cortinovis D., Dominici M., Pesci A., Bargagli E., Luppi F., Cameli, P, Faverio, P, Ferrari, K, Bonti, V, Marsili, S, Mazzei, M, Mazzoni, F, Bartolucci, M, Scotti, V, Bertolini, F, Barbieri, F, Baldessari, C, Veronese, C, Boffi, R, Brighenti, M, Cortinovis, D, Dominici, M, Pesci, A, Bargagli, E, Luppi, F, Cameli P., Faverio P., Ferrari K., Bonti V., Marsili S., Mazzei M. A., Mazzoni F., Bartolucci M., Scotti V., Bertolini F., Barbieri F., Baldessari C., Veronese C., Boffi R., Brighenti M., Cortinovis D., Dominici M., Pesci A., Bargagli E., and Luppi F.

Abstract

Background: Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic horizons of various cancers. However, immune-related adverse events have been reported, including interstitial lung diseases. Our aim was to describe the clinical and radiological features and survival of a multicentre cohort of patients who developed ICI-related lung toxicity. Methods: Six Italian centres were involved in the study. Patients who were treated with anti-PD-1/PD-L1 and CTLA-4 mAbs and developed ICI-related lung toxicity were recruited retrospectively to study clinical, radiological, immunological and survival data. Results: A total of 41 patients (25 males, 66.8 ± 9.9 years) were enrolled. Lung toxicity occurred after 204.3 ± 208.3 days of therapy, with ground glass opacities being the most common HRCT pattern (23 cases). Male sex, lung cancer and acute respiratory failure were associated with a shorter latency of toxicity (p = 0.0030, p = 0.0245 and p = 0.0390, respectively). Patients who required high-flow oxygen therapy showed significantly worse survival (p = 0.0028). Conclusions: Our cohort showed heterogeneous clinical and radiological aspects of ICI-related lung toxicity, with a latency not limited to the first year of treatment. Severity was mainly mild to moderate, although life-threatening events did occur. Our data indicate that strict long-term follow-up is needed to enable early diagnosis and appropriate management.

Published
2022

26. Mispecification tests for hidden Markov models based on a new class of finite mixture models

Author

CLAD - Associação Portuguesa de Classificação e Análise de Dados, Brito, P, Bartolucci, F, Pandolfi, F, Pennoni, F, Bartolucci F, Pandolfi F, Pennoni F, CLAD - Associação Portuguesa de Classificação e Análise de Dados, Brito, P, Bartolucci, F, Pandolfi, F, Pennoni, F, Bartolucci F, Pandolfi F, and Pennoni F

Abstract

In the context of longitudinal data, we show that a general class of hidden Markov (HM, [1]) models may be equivalent to a class of finite mixture (FM, [3]) models based on an augmented set of components and suitable constraints on the conditional response probabilities, given these components. We formulate a misspecification test for the latent structure of an HM model comparing maximum likelihood values of the two models for the same data, and when the number of possible latent state sequences is excessive, we propose a multiple version of this test including the Bonferroni correction. The procedure is simple since it is based on the output of the Expectation-Maximization estimation algorithm [2]. The properties of this testing procedure are evaluated through a simulation study. An empirical application illustrates it through data from the National Longitudinal Survey of Youth, in which we jointly consider wages and years of experience after labour force entry. We show that the proposed testing procedure may also be used as an alternative model selection criterion for the number of latent states of an HM model to those usually employed.

Published
2022

27. Alternative methods for parameter estimation in discrete latent variable models

Author

Brusa, L, Bartolucci, F, Pennoni, F, Luca Brusa, Francesco Bartolucci, Fulvia Pennoni, Brusa, L, Bartolucci, F, Pennoni, F, Luca Brusa, Francesco Bartolucci, and Fulvia Pennoni

Abstract

The Expectation-Maximization (EM) algorithm is undoubtedly one of the most widely used techniques to estimate a discrete latent variable (DLV) model. However, while it is possible to prove that this algorithm converges to a local maximum of the log-likelihood function, there is no guarantee of convergence to the global maximum of this function. We propose two modifications to the EM algorithm to tackle this serious problem. The first one incorporates a tempering scheme into the EM algorithm: the log-likelihood is initially flattened to escape local maxima and then warped back to its original shape in a gradual way. The second uses evolutionary computation to encourage more accurate parameter space exploration. The performance of the resulting tempered EM (T-EM) and evolutionary EM (E-EM) algorithms is assessed for latent class and hidden Markov models in terms of both ability to reach the global maximum and computational time; a comparison with the standard EM algorithm is carried out through an extensive Monte Carlo simulation study. We show that the proposed algorithms outperform the standard EM, significantly increasing the chance of reaching the global maximum in almost all the examined cases. This improvement remains considerable, even accounting for the inflated overall computing time.

Published
2022

28. Language production impairments in patients with a first episode of psychosis

Author

Gargano, G, Caletti, E, Perlini, C, Turtulici, N, Bellani, M, Bonivento, C, Garzitto, M, Siri, F, Longo, C, Bonetto, C, Cristofalo, D, Scocco, P, Semrov, E, Preti, A, Lazzarotto, L, Gardellin, F, Lasalvia, A, Ruggeri, M, Marini, A, Brambilla, P, Bertani, M, Bissoli, S, De Santi, K, Lunardi, S, Negretto, V, Poli, S, Tosato, S, Zamboni, M, Ballarin, M, De Girolamo, G, Fioritti, A, Neri, G, Pileggi, F, Rucci, P, Chiavetto, L, Scasselatti, C, Zanardini, R, Bertoldo, A, Marinelli, V, Rambaldelli, G, Bardella, S, Lamonaca, D, Lunardon, M, Magnabosco, R, Martucci, M, Nicolau, S, Nifosi, F, Pavanati, M, Rossi, M, Piazza, C, Piccione, G, Sala, A, Sale, A, Stefani, B, Zotos, S, Balbo, M, Boggian, I, Ceccato, E, Dall'Agnola, R, Girotto, B, Goss, C, Leoni, R, Mai, A, Pasqualini, A, Roccato, S, Rossi, A, Strizzolo, S, Urbani, A, Aldi, F, Bianchi, B, Cappellari, P, Conti, R, De Battisti, L, Lazzarin, E, Merlin, S, Migliorini, G, Pozzan, T, Sarto, L, Visona, S, Brazzoli, A, Campi, A, Carmagnani, R, Giambelli, S, Gianella, A, Lunardi, L, Madaghiele, D, Maestrelli, P, Paiola, L, Posteri, E, Viola, L, Zamberlan, V, Zenari, M, Zanoni, M, Bonadonna, G, Bonomo, M, Santonastaso, P, Cremonese, C, Veronese, A, Anderle, P, Angelozzi, A, Baron, I, Candeago, E, Castelli, F, Chieco, M, Di Costanzo, E, Derossi, M, Doriguzzi, M, Galvano, O, Lattanzi, M, Lezzi, R, Marcato, M, Marcolin, A, Marini, F, Matranga, M, Scalabrin, D, Zucchetto, M, Zadro, F, Austoni, G, Bianco, M, Bordino, F, Dario, F, De Risio, A, Gatto, A, Grana, S, Favero, E, Franceschini, A, Friederici, S, Marangon, V, Pascolo, M, Ramon, L, Zambolin, S, Riolo, R, Buffon, A, Di Bortolo, E, Fortin, S, Matarrese, F, Mogni, S, Codemo, N, Russi, A, Silvestro, A, Turella, E, Viel, P, Dominoni, A, Andreose, L, Boemio, M, Bressan, L, Cabbia, A, Canesso, E, Cian, R, Dal Piccol, C, Pasqua, M, Di Prisco, A, Mantellato, L, Luison, M, Morgante, S, Santi, M, Sacillotto, M, Scabbio, M, Sponga, P, Sguotto, M, Stach, F, Vettorato, M, Martinello, G, Dassie, F, Marino, S, Cibiniel, L, Masetto, I, Cabianca, O, Valente, A, Caberlotto, L, Passoni, A, Flumian, P, Daniel, L, Gion, M, Stanziale, S, Alborino, F, Bortolozzo, V, Bacelle, L, Bicciato, L, Basso, D, Navaglia, F, Manoni, F, Ercolin, M, Giubilini, F, Imbesi, M, Leuci, E, Mazzi, F, Anelli, S, Amore, M, Bigi, L, Britta, W, Anna, G, Bonatti, U, Borziani, M, Crosato, S, Fabris, I, Galluccio, R, Galeotti, M, Gozzi, M, Greco, V, Guagnini, E, Pagani, S, Maccherozzi, S, Malvasi, R, Marchi, F, Melato, E, Mazzucchi, E, Marzullo, F, Pellegrini, P, Petrolini, N, Volta, P, Bonara, F, Brusamonti, E, Croci, R, Flamia, I, Fontana, F, Losi, R, Marchioro, R, Raffaini, L, Ruju, L, Saginario, A, Tondelli, M, Marrama, D, Bernardelli, L, Bonacini, F, Florindo, A, Merli, M, Nappo, P, Sola, L, Tondelli, O, Tonna, M, Torre, M, Tosatti, M, Venturelli, G, Zampolla, D, Bernardi, A, Cavalli, C, Cigala, L, Ciraudo, C, Di Bari, A, Ferri, L, Gombi, F, Leurini, S, Mandatelli, E, Maccaferri, S, Oroboncoide, M, Pisa, B, Ricci, C, Poggi, E, Zurlini, C, Malpeli, M, Colla, R, Teodori, E, Vecchia, L, D'Andrea, R, Trenti, T, Paolini, P, Carpeggiani, P, Ghigi, D, Gagliostro, M, Pratelli, M, Antonelli, A, Battistini, L, Bellini, F, Bonini, E, Capelli, C, Didomizio, C, Drei, C, Fucci, G, Gualandi, A, Grazia, M, Losi, A, Mazzoni, F, Marangoni, D, Monna, G, Morselli, M, Oggioni, A, Oprandi, S, Paganelli, W, Passerini, M, Piscitelli, M, Reggiani, G, Rossi, G, Salvatori, F, Trasforini, S, Uslenghi, C, Veggetti, S, Bartolucci, G, Baruffa, R, Bertelli, R, Borghi, L, Ciavarella, P, Paltrinieri, E, Rizzardi, F, Serra, P, Suzzi, D, Arienti, P, Aureli, F, Avanzi, R, Callegari, V, Corsino, A, Host, P, Michetti, R, Rizzo, F, Simoncelli, P, Soldati, E, Succi, E, Bertozzi, M, Canetti, E, Cavicchioli, L, Ceccarelli, E, Cenni, S, Marzola, G, Gallina, V, Leoni, C, Olivieri, A, Piccolo, E, Ravagli, S, Russo, R, Tedeschini, D, Verenini, M, Abram, W, Granata, V, Curcio, A, Guerra, G, Granini, S, Natali, L, Montanari, E, Pasi, F, Ventura, U, Valenti, S, Francesca, M, Farneti, R, Ravagli, P, Floris, R, Maroncelli, O, Volpones, G, Casali, D, Miceli, M, Bencini, A, Cellini, M, De Biase, L, Barbara, L, Charles, L, Pratesi, C, Tanini, A, Loparrino, R, Ulivelli, C, Cussoto, C, Dei, N, Fumanti, E, Pantani, M, Zeloni, G, Bellini, R, Cellesi, R, Dorigo, N, Gulli, P, Ialeggio, L, Pisanu, M, Rinaldi, G, Konze, A, Cocchi, A, Meneghelli, A, Frova, M, Monzani, E, Zanobio, A, Malagoli, M, Pagani, R, Barbera, S, Morganti, C, Amade, E, Brambilla, V, Montanari, A, Caterina, G, Lopez, C, Marocchi, A, Moletta, A, Sberna, M, Cascio, M, Scarone, S, Gargano G., Caletti E., Perlini C., Turtulici N., Bellani M., Bonivento C., Garzitto M., Siri F. M., Longo C., Bonetto C., Cristofalo D., Scocco P., Semrov E., Preti A., Lazzarotto L., Gardellin F., Lasalvia A., Ruggeri M., Marini A., Brambilla P., Bertani M. E., Bissoli S., De Santi K., Lunardi S., Negretto V., Poli S., Tosato S., Zamboni M. G., Ballarin M., De Girolamo G., Fioritti A., Neri G., Pileggi F., Rucci P., Chiavetto L. B., Scasselatti C., Zanardini R., Bertoldo A., Marinelli V., Rambaldelli G., Bardella S., Lamonaca D., Lunardon M., Magnabosco R., Martucci M., Nicolau S., Nifosi F., Pavanati M., Rossi M., Piazza C., Piccione G., Sala A., Sale A., Stefani B., Zotos S., Balbo M., Boggian I., Ceccato E., Dall'Agnola R., Girotto B., Goss C., Leoni R., Mai A., Pasqualini A., Roccato S., Rossi A., Strizzolo S., Urbani A., Aldi F., Bianchi B., Cappellari P., Conti R., De Battisti L., Lazzarin E., Merlin S., Migliorini G., Pozzan T., Sarto L., Visona S., Brazzoli A., Campi A., Carmagnani R., Giambelli S., Gianella A., Lunardi L., Madaghiele D., Maestrelli P., Paiola L., Posteri E., Viola L., Zamberlan V., Zenari M., Zanoni M., Bonadonna G., Bonomo M., Santonastaso P., Cremonese C., Veronese A., Anderle P., Angelozzi A., Baron I. A. G., Candeago E. B. F., Castelli F., Chieco M., Di Costanzo E., Derossi M., Doriguzzi M., Galvano O., Lattanzi M., Lezzi R., Marcato M., Marcolin A., Marini F., Matranga M., Scalabrin D., Zucchetto M., Zadro F., Austoni G., Bianco M., Bordino F., Dario F., De Risio A., Gatto A., Grana S., Favero E., Franceschini A., Friederici S., Marangon V., Pascolo M., Ramon L., Zambolin S., Riolo R., Buffon A., Di Bortolo E., Fortin S., Matarrese F., Mogni S., Codemo N., Russi A., Silvestro A., Turella E., Viel P., Dominoni A., Andreose L., Boemio M., Bressan L., Cabbia A., Canesso E., Cian R., Dal Piccol C., Pasqua M. M. D., Di Prisco A., Mantellato L., Luison M., Morgante S., Santi M., Sacillotto M., Scabbio M., Sponga P., Sguotto M., Stach F., Vettorato M., Martinello G., Dassie F., Marino S., Cibiniel L., Masetto I., Cabianca O., Valente A., Caberlotto L., Passoni A., Flumian P., Daniel L., Gion M., Stanziale S., Alborino F., Bortolozzo V., Bacelle L., Bicciato L., Basso D., Navaglia F., Manoni F., Ercolin M., Giubilini F., Imbesi M., Leuci E., Mazzi F., Anelli S., Amore M., Bigi L., Britta W., Anna G. B., Bonatti U., Borziani M., Crosato S., Fabris I., Galluccio R., Galeotti M., Gozzi M., Greco V., Guagnini E., Pagani S., Maccherozzi S., Malvasi R., Marchi F., Melato E., Mazzucchi E., Marzullo F., Pellegrini P., Petrolini N., Volta P., Bonara F., Brusamonti E., Croci R., Flamia I., Fontana F., Losi R., Marchioro R., Raffaini L., Ruju L., Saginario A., Tondelli M., Marrama D., Bernardelli L., Bonacini F., Florindo A., Merli M., Nappo P., Sola L., Tondelli O., Tonna M., Torre M., Tosatti M., Venturelli G., Zampolla D., Bernardi A., Cavalli C., Cigala L., Ciraudo C., Di Bari A., Ferri L., Gombi F., Leurini S., Mandatelli E., Maccaferri S., Oroboncoide M., Pisa B., Ricci C., Poggi E., Zurlini C., Malpeli M., Colla R., Teodori E., Vecchia L., D'Andrea R., Trenti T., Paolini P., Carpeggiani P., Ghigi D., Gagliostro M., Pratelli M., Antonelli A., Battistini L., Bellini F., Bonini E., Capelli C. B. R., DiDomizio C., Drei C., Fucci G., Gualandi A., Grazia M. R., Losi A. M., Mazzoni F. M. P., Marangoni D., Monna G., Morselli M., Oggioni A., Oprandi S., Paganelli W., Passerini M., Piscitelli M., Reggiani G., Rossi G., Salvatori F., Trasforini S., Uslenghi C., Veggetti S., Bartolucci G., Baruffa R., Bertelli R., Borghi L., Ciavarella P., Paltrinieri E., Rizzardi F., Serra P., Suzzi D., Arienti P., Aureli F., Avanzi R., Callegari V., Corsino A., Host P., Michetti R., Rizzo F., Simoncelli P., Soldati E., Succi E., Bertozzi M., Canetti E., Cavicchioli L., Ceccarelli E., Cenni S., Marzola G., Gallina V., Leoni C., Olivieri A., Piccolo E., Ravagli S., Russo R., Tedeschini D., Verenini M., Abram W., Granata V., Curcio A., Guerra G., Granini S., Natali L., Montanari E., Pasi F., Ventura U., Valenti S., Francesca M., Farneti R., Ravagli P., Floris R., Maroncelli O., Volpones G., Casali D., Miceli M., Bencini A., Cellini M., De Biase L., Barbara L., Charles L., Pratesi C., Tanini A., Loparrino R., Ulivelli C., Cussoto C., Dei N., Fumanti E., Pantani M., Zeloni G., Bellini R., Cellesi R., Dorigo N., Gulli P., Ialeggio L., Pisanu M., Rinaldi G., Konze A., Cocchi A., Meneghelli A., Frova M., Monzani E., Zanobio A., Malagoli M., Pagani R., Barbera S., Morganti C., Amade E. S., Brambilla V., Montanari A., Caterina G., Lopez C., Marocchi A., Moletta A., Sberna M., Cascio M. T., Scarone S., Gargano, G, Caletti, E, Perlini, C, Turtulici, N, Bellani, M, Bonivento, C, Garzitto, M, Siri, F, Longo, C, Bonetto, C, Cristofalo, D, Scocco, P, Semrov, E, Preti, A, Lazzarotto, L, Gardellin, F, Lasalvia, A, Ruggeri, M, Marini, A, Brambilla, P, Bertani, M, Bissoli, S, De Santi, K, Lunardi, S, Negretto, V, Poli, S, Tosato, S, Zamboni, M, Ballarin, M, De Girolamo, G, Fioritti, A, Neri, G, Pileggi, F, Rucci, P, Chiavetto, L, Scasselatti, C, Zanardini, R, Bertoldo, A, Marinelli, V, Rambaldelli, G, Bardella, S, Lamonaca, D, Lunardon, M, Magnabosco, R, Martucci, M, Nicolau, S, Nifosi, F, Pavanati, M, Rossi, M, Piazza, C, Piccione, G, Sala, A, Sale, A, Stefani, B, Zotos, S, Balbo, M, Boggian, I, Ceccato, E, Dall'Agnola, R, Girotto, B, Goss, C, Leoni, R, Mai, A, Pasqualini, A, Roccato, S, Rossi, A, Strizzolo, S, Urbani, A, Aldi, F, Bianchi, B, Cappellari, P, Conti, R, De Battisti, L, Lazzarin, E, Merlin, S, Migliorini, G, Pozzan, T, Sarto, L, Visona, S, Brazzoli, A, Campi, A, Carmagnani, R, Giambelli, S, Gianella, A, Lunardi, L, Madaghiele, D, Maestrelli, P, Paiola, L, Posteri, E, Viola, L, Zamberlan, V, Zenari, M, Zanoni, M, Bonadonna, G, Bonomo, M, Santonastaso, P, Cremonese, C, Veronese, A, Anderle, P, Angelozzi, A, Baron, I, Candeago, E, Castelli, F, Chieco, M, Di Costanzo, E, Derossi, M, Doriguzzi, M, Galvano, O, Lattanzi, M, Lezzi, R, Marcato, M, Marcolin, A, Marini, F, Matranga, M, Scalabrin, D, Zucchetto, M, Zadro, F, Austoni, G, Bianco, M, Bordino, F, Dario, F, De Risio, A, Gatto, A, Grana, S, Favero, E, Franceschini, A, Friederici, S, Marangon, V, Pascolo, M, Ramon, L, Zambolin, S, Riolo, R, Buffon, A, Di Bortolo, E, Fortin, S, Matarrese, F, Mogni, S, Codemo, N, Russi, A, Silvestro, A, Turella, E, Viel, P, Dominoni, A, Andreose, L, Boemio, M, Bressan, L, Cabbia, A, Canesso, E, Cian, R, Dal Piccol, C, Pasqua, M, Di Prisco, A, Mantellato, L, Luison, M, Morgante, S, Santi, M, Sacillotto, M, Scabbio, M, Sponga, P, Sguotto, M, Stach, F, Vettorato, M, Martinello, G, Dassie, F, Marino, S, Cibiniel, L, Masetto, I, Cabianca, O, Valente, A, Caberlotto, L, Passoni, A, Flumian, P, Daniel, L, Gion, M, Stanziale, S, Alborino, F, Bortolozzo, V, Bacelle, L, Bicciato, L, Basso, D, Navaglia, F, Manoni, F, Ercolin, M, Giubilini, F, Imbesi, M, Leuci, E, Mazzi, F, Anelli, S, Amore, M, Bigi, L, Britta, W, Anna, G, Bonatti, U, Borziani, M, Crosato, S, Fabris, I, Galluccio, R, Galeotti, M, Gozzi, M, Greco, V, Guagnini, E, Pagani, S, Maccherozzi, S, Malvasi, R, Marchi, F, Melato, E, Mazzucchi, E, Marzullo, F, Pellegrini, P, Petrolini, N, Volta, P, Bonara, F, Brusamonti, E, Croci, R, Flamia, I, Fontana, F, Losi, R, Marchioro, R, Raffaini, L, Ruju, L, Saginario, A, Tondelli, M, Marrama, D, Bernardelli, L, Bonacini, F, Florindo, A, Merli, M, Nappo, P, Sola, L, Tondelli, O, Tonna, M, Torre, M, Tosatti, M, Venturelli, G, Zampolla, D, Bernardi, A, Cavalli, C, Cigala, L, Ciraudo, C, Di Bari, A, Ferri, L, Gombi, F, Leurini, S, Mandatelli, E, Maccaferri, S, Oroboncoide, M, Pisa, B, Ricci, C, Poggi, E, Zurlini, C, Malpeli, M, Colla, R, Teodori, E, Vecchia, L, D'Andrea, R, Trenti, T, Paolini, P, Carpeggiani, P, Ghigi, D, Gagliostro, M, Pratelli, M, Antonelli, A, Battistini, L, Bellini, F, Bonini, E, Capelli, C, Didomizio, C, Drei, C, Fucci, G, Gualandi, A, Grazia, M, Losi, A, Mazzoni, F, Marangoni, D, Monna, G, Morselli, M, Oggioni, A, Oprandi, S, Paganelli, W, Passerini, M, Piscitelli, M, Reggiani, G, Rossi, G, Salvatori, F, Trasforini, S, Uslenghi, C, Veggetti, S, Bartolucci, G, Baruffa, R, Bertelli, R, Borghi, L, Ciavarella, P, Paltrinieri, E, Rizzardi, F, Serra, P, Suzzi, D, Arienti, P, Aureli, F, Avanzi, R, Callegari, V, Corsino, A, Host, P, Michetti, R, Rizzo, F, Simoncelli, P, Soldati, E, Succi, E, Bertozzi, M, Canetti, E, Cavicchioli, L, Ceccarelli, E, Cenni, S, Marzola, G, Gallina, V, Leoni, C, Olivieri, A, Piccolo, E, Ravagli, S, Russo, R, Tedeschini, D, Verenini, M, Abram, W, Granata, V, Curcio, A, Guerra, G, Granini, S, Natali, L, Montanari, E, Pasi, F, Ventura, U, Valenti, S, Francesca, M, Farneti, R, Ravagli, P, Floris, R, Maroncelli, O, Volpones, G, Casali, D, Miceli, M, Bencini, A, Cellini, M, De Biase, L, Barbara, L, Charles, L, Pratesi, C, Tanini, A, Loparrino, R, Ulivelli, C, Cussoto, C, Dei, N, Fumanti, E, Pantani, M, Zeloni, G, Bellini, R, Cellesi, R, Dorigo, N, Gulli, P, Ialeggio, L, Pisanu, M, Rinaldi, G, Konze, A, Cocchi, A, Meneghelli, A, Frova, M, Monzani, E, Zanobio, A, Malagoli, M, Pagani, R, Barbera, S, Morganti, C, Amade, E, Brambilla, V, Montanari, A, Caterina, G, Lopez, C, Marocchi, A, Moletta, A, Sberna, M, Cascio, M, Scarone, S, Gargano G., Caletti E., Perlini C., Turtulici N., Bellani M., Bonivento C., Garzitto M., Siri F. M., Longo C., Bonetto C., Cristofalo D., Scocco P., Semrov E., Preti A., Lazzarotto L., Gardellin F., Lasalvia A., Ruggeri M., Marini A., Brambilla P., Bertani M. E., Bissoli S., De Santi K., Lunardi S., Negretto V., Poli S., Tosato S., Zamboni M. G., Ballarin M., De Girolamo G., Fioritti A., Neri G., Pileggi F., Rucci P., Chiavetto L. B., Scasselatti C., Zanardini R., Bertoldo A., Marinelli V., Rambaldelli G., Bardella S., Lamonaca D., Lunardon M., Magnabosco R., Martucci M., Nicolau S., Nifosi F., Pavanati M., Rossi M., Piazza C., Piccione G., Sala A., Sale A., Stefani B., Zotos S., Balbo M., Boggian I., Ceccato E., Dall'Agnola R., Girotto B., Goss C., Leoni R., Mai A., Pasqualini A., Roccato S., Rossi A., Strizzolo S., Urbani A., Aldi F., Bianchi B., Cappellari P., Conti R., De Battisti L., Lazzarin E., Merlin S., Migliorini G., Pozzan T., Sarto L., Visona S., Brazzoli A., Campi A., Carmagnani R., Giambelli S., Gianella A., Lunardi L., Madaghiele D., Maestrelli P., Paiola L., Posteri E., Viola L., Zamberlan V., Zenari M., Zanoni M., Bonadonna G., Bonomo M., Santonastaso P., Cremonese C., Veronese A., Anderle P., Angelozzi A., Baron I. A. G., Candeago E. B. F., Castelli F., Chieco M., Di Costanzo E., Derossi M., Doriguzzi M., Galvano O., Lattanzi M., Lezzi R., Marcato M., Marcolin A., Marini F., Matranga M., Scalabrin D., Zucchetto M., Zadro F., Austoni G., Bianco M., Bordino F., Dario F., De Risio A., Gatto A., Grana S., Favero E., Franceschini A., Friederici S., Marangon V., Pascolo M., Ramon L., Zambolin S., Riolo R., Buffon A., Di Bortolo E., Fortin S., Matarrese F., Mogni S., Codemo N., Russi A., Silvestro A., Turella E., Viel P., Dominoni A., Andreose L., Boemio M., Bressan L., Cabbia A., Canesso E., Cian R., Dal Piccol C., Pasqua M. M. D., Di Prisco A., Mantellato L., Luison M., Morgante S., Santi M., Sacillotto M., Scabbio M., Sponga P., Sguotto M., Stach F., Vettorato M., Martinello G., Dassie F., Marino S., Cibiniel L., Masetto I., Cabianca O., Valente A., Caberlotto L., Passoni A., Flumian P., Daniel L., Gion M., Stanziale S., Alborino F., Bortolozzo V., Bacelle L., Bicciato L., Basso D., Navaglia F., Manoni F., Ercolin M., Giubilini F., Imbesi M., Leuci E., Mazzi F., Anelli S., Amore M., Bigi L., Britta W., Anna G. B., Bonatti U., Borziani M., Crosato S., Fabris I., Galluccio R., Galeotti M., Gozzi M., Greco V., Guagnini E., Pagani S., Maccherozzi S., Malvasi R., Marchi F., Melato E., Mazzucchi E., Marzullo F., Pellegrini P., Petrolini N., Volta P., Bonara F., Brusamonti E., Croci R., Flamia I., Fontana F., Losi R., Marchioro R., Raffaini L., Ruju L., Saginario A., Tondelli M., Marrama D., Bernardelli L., Bonacini F., Florindo A., Merli M., Nappo P., Sola L., Tondelli O., Tonna M., Torre M., Tosatti M., Venturelli G., Zampolla D., Bernardi A., Cavalli C., Cigala L., Ciraudo C., Di Bari A., Ferri L., Gombi F., Leurini S., Mandatelli E., Maccaferri S., Oroboncoide M., Pisa B., Ricci C., Poggi E., Zurlini C., Malpeli M., Colla R., Teodori E., Vecchia L., D'Andrea R., Trenti T., Paolini P., Carpeggiani P., Ghigi D., Gagliostro M., Pratelli M., Antonelli A., Battistini L., Bellini F., Bonini E., Capelli C. B. R., DiDomizio C., Drei C., Fucci G., Gualandi A., Grazia M. R., Losi A. M., Mazzoni F. M. P., Marangoni D., Monna G., Morselli M., Oggioni A., Oprandi S., Paganelli W., Passerini M., Piscitelli M., Reggiani G., Rossi G., Salvatori F., Trasforini S., Uslenghi C., Veggetti S., Bartolucci G., Baruffa R., Bertelli R., Borghi L., Ciavarella P., Paltrinieri E., Rizzardi F., Serra P., Suzzi D., Arienti P., Aureli F., Avanzi R., Callegari V., Corsino A., Host P., Michetti R., Rizzo F., Simoncelli P., Soldati E., Succi E., Bertozzi M., Canetti E., Cavicchioli L., Ceccarelli E., Cenni S., Marzola G., Gallina V., Leoni C., Olivieri A., Piccolo E., Ravagli S., Russo R., Tedeschini D., Verenini M., Abram W., Granata V., Curcio A., Guerra G., Granini S., Natali L., Montanari E., Pasi F., Ventura U., Valenti S., Francesca M., Farneti R., Ravagli P., Floris R., Maroncelli O., Volpones G., Casali D., Miceli M., Bencini A., Cellini M., De Biase L., Barbara L., Charles L., Pratesi C., Tanini A., Loparrino R., Ulivelli C., Cussoto C., Dei N., Fumanti E., Pantani M., Zeloni G., Bellini R., Cellesi R., Dorigo N., Gulli P., Ialeggio L., Pisanu M., Rinaldi G., Konze A., Cocchi A., Meneghelli A., Frova M., Monzani E., Zanobio A., Malagoli M., Pagani R., Barbera S., Morganti C., Amade E. S., Brambilla V., Montanari A., Caterina G., Lopez C., Marocchi A., Moletta A., Sberna M., Cascio M. T., and Scarone S.

Abstract

Language production has often been described as impaired in psychiatric diseases such as in psychosis. Nevertheless, little is known about the characteristics of linguistic difficulties and their relation with other cognitive domains in patients with a first episode of psychosis (FEP), either affective or non-affective. To deepen our comprehension of linguistic profile in FEP, 133 patients with FEP (95 non-affective, FEP-NA; 38 affective, FEP-A) and 133 healthy controls (HC) were assessed with a narrative discourse task. Speech samples were systematically analyzed with a well-established multilevel procedure investigating both micro- (lexicon, morphology, syntax) and macro-linguistic (discourse coherence, pragmatics) levels of linguistic processing. Executive functioning and IQ were also evaluated. Both linguistic and neuropsychological measures were secondarily implemented with a machine learning approach in order to explore their predictive accuracy in classifying participants as FEP or HC. Compared to HC, FEP patients showed language production difficulty at both micro- and macro-linguistic levels. As for the former, FEP produced shorter and simpler sentences and fewer words per minute, along with a reduced number of lexical fillers, compared to HC. At the macro-linguistic level, FEP performance was impaired in local coherence, which was paired with a higher percentage of utterances with semantic errors. Linguistic measures were not correlated with any neuropsychological variables. No significant differences emerged between FEP-NA and FEP-A (p≥0.02, after Bonferroni correction). Machine learning analysis showed an accuracy of group prediction of 76.36% using language features only, with semantic variables being the most impactful. Such a percentage was enhanced when paired with clinical and neuropsychological variables. Results confirm the presence of language production deficits already at the first episode of the illness, being such impairment not related to ot

Published
2022

29. Exploring the dependencies among main cryptocurrency log-returns: A hidden Markov model

Author

Pennoni, F, Bartolucci, F, Forte, G, Ametrano, F, Pennoni F., Bartolucci F., Forte G., Ametrano F., Pennoni, F, Bartolucci, F, Forte, G, Ametrano, F, Pennoni F., Bartolucci F., Forte G., and Ametrano F.

Abstract

A hidden Markov model is proposed for the analysis of time-series of daily log-returns of the last 4 years of Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash. These log-returns are assumed to have a multivariate Gaussian distribution conditionally on a latent Markov process having a finite number of regimes or states. The hidden regimes represent different market phases identified through distinct vectors of expected values and variance–covariance matrices of the log-returns, so that they also differ in terms of volatility. Maximum-likelihood estimation of the model parameters is carried out by the expectation–maximisation algorithm, and regimes are singularly predicted for every time occasion according to the maximum-a-posteriori rule. Results show three positive and three negative phases of the market. In the most recent period, an increasing tendency towards positive regimes is also predicted. A rather heterogeneous correlation structure is estimated, and evidence of structural medium term trend in the correlation of Bitcoin with the other cryptocurrencies is detected.

Published
2022

30. A Regime switching Student-t copula model for the analysis of cryptocurrencies data

Author

Marco Corazza, Cira Perna, Claudio Pizzi, Marilena Sibillo, Magni, G, Bimonte, G, Naimoli, A, Cortese, F, Bartolucci, F, Pennoni, F, Cortese, F., Bartolucci, F., Pennoni, F., Marco Corazza, Cira Perna, Claudio Pizzi, Marilena Sibillo, Magni, G, Bimonte, G, Naimoli, A, Cortese, F, Bartolucci, F, Pennoni, F, Cortese, F., Bartolucci, F., and Pennoni, F.

Abstract

Flexible statistical models have an important role in explaining the joint distribution of financial returns. In these analyses, it is necessary to consider abrupt switches in the market conditions, especially if the focus is on cryptoassets, the market of which is characterized by high instabilities. Regime switching (RS) copula models represent a powerful tool to formulate the joint distribution of time-series accurately: they are based on a copula distribution with parameters governed by a hidden Markov process of first-order so as to account for the correlation patterns between series. The hidden states represent different market regimes, each described by a state-specific vector of copula parameters. We propose RS copula models as a valuable instrument for describing the joint behavior of log- returns. We choose a Student-t copula function to consider extreme dependent values appropriately as they are often observed in financial returns. We split the modeling process into two steps: the first one consists in fitting the marginal distribution of each univariate time-series, while the second one deals with the estimation of the joint distribution of the log-returns described by a RS copula model. Maximum likelihood estimation of the model parameters is carried out by the expectation-maximization (EM) algorithm, which alternates two steps until convergence: at the E-step, we compute the expectation of the log-likelihood evaluated using the current values for the parameters and, at the M-step, parameters estimates are updated by maximizing the expected complete-data log-likelihood computed at the previous step. The main computational burdens deal with estimating the correlation matrix (R) and the number of degrees of freedom (v) of the Student t-copula. At this aim, we propose performing the M-step by computing R given v using a closed form solution obtained from a constrained optimization of the log-likelihood using Lagrange multipliers. Then, we numerically maxim

Published
2022

31. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Author

Sherratt, K., Gruson, H., Grah, R., Johnson, H., Niehus, R., Prasse, B., Sandmann, F., Deuschel, J., Wolffram, D., Abbott, S., Ullrich, A., Gibson, G., Ray, E. L., Reich, N. G., Sheldon, D., Wang, Y., Wattanachit, N., Wang, L., Trnka, J., Obozinski, G., Sun, T., Thanou, D., Pottier, L., Krymova, E., Meinke, J. H., Barbarossa, M. V., Leithäuser, N., Mohring, J., Schneider, J., Wlazlo, J., Fuhrmann, J., Lange, B., Rodiah, I., Baccam, P., Gurung, H., Stage, S., Suchoski, B., Budzinski, J., Walraven, R., Villanueva, I., Tucek, V., Smíd, M., Zajícek, M., Pérez Alvarez, C., Reina, B., Bosse, N. I., Meakin, S., Castro, L., Fairchild, G., Michaud, I., Osthus, D., Alaimo Di Loro, P., Maruotti, A., Eclerová, V., Kraus, A., Kraus, D., Pribylova, L., Dimitris, B., Li, M. L., Saksham, S., Dehning, J., Mohr, S., Priesemann, V., Redlarski, G., Bejar, B., Ardenghi, G., Parolini, N., Ziarelli, G., Bock, Wolfgang, Heyder, S., Hotz, T., E. Singh, D., Guzman-Merino, M., Aznarte, J. L., Moriña, D., Alonso, S., Alvarez, E., López, D., Prats, C., Burgard, J. P., Rodloff, A., Zimmermann, T., Kuhlmann, A., Zibert, J., Pennoni, F., Divino, F., Català, M., Lovison, G., Giudici, P., Tarantino, B., Bartolucci, F., Jona Lasinio, G., Mingione, M., Farcomeni, A., Srivastava, A., Montero-Manso, P., Adiga, A., Hurt, B., Lewis, B., Marathe, M., Porebski, P., Venkatramanan, S., Bartczuk, R., Dreger, F., Gambin, A., Gogolewski, K., Gruziel-S?omka, M., Krupa, B., Moszynski, A., Niedzielewski, K., Nowosielski, J., Radwan, M., Rakowski, F., Semeniuk, M., Szczurek, E., Zieli?ski, J., Kisielewski, J., Pabjan, B., Kheifetz, Y., Kirsten, H., Scholz, M., Biecek, P., Bodych, M., Filinski, M., Idzikowski, R., Krueger, T., Ozanski, T., Bracher, J., Funk, S., Sherratt, K., Gruson, H., Grah, R., Johnson, H., Niehus, R., Prasse, B., Sandmann, F., Deuschel, J., Wolffram, D., Abbott, S., Ullrich, A., Gibson, G., Ray, E. L., Reich, N. G., Sheldon, D., Wang, Y., Wattanachit, N., Wang, L., Trnka, J., Obozinski, G., Sun, T., Thanou, D., Pottier, L., Krymova, E., Meinke, J. H., Barbarossa, M. V., Leithäuser, N., Mohring, J., Schneider, J., Wlazlo, J., Fuhrmann, J., Lange, B., Rodiah, I., Baccam, P., Gurung, H., Stage, S., Suchoski, B., Budzinski, J., Walraven, R., Villanueva, I., Tucek, V., Smíd, M., Zajícek, M., Pérez Alvarez, C., Reina, B., Bosse, N. I., Meakin, S., Castro, L., Fairchild, G., Michaud, I., Osthus, D., Alaimo Di Loro, P., Maruotti, A., Eclerová, V., Kraus, A., Kraus, D., Pribylova, L., Dimitris, B., Li, M. L., Saksham, S., Dehning, J., Mohr, S., Priesemann, V., Redlarski, G., Bejar, B., Ardenghi, G., Parolini, N., Ziarelli, G., Bock, Wolfgang, Heyder, S., Hotz, T., E. Singh, D., Guzman-Merino, M., Aznarte, J. L., Moriña, D., Alonso, S., Alvarez, E., López, D., Prats, C., Burgard, J. P., Rodloff, A., Zimmermann, T., Kuhlmann, A., Zibert, J., Pennoni, F., Divino, F., Català, M., Lovison, G., Giudici, P., Tarantino, B., Bartolucci, F., Jona Lasinio, G., Mingione, M., Farcomeni, A., Srivastava, A., Montero-Manso, P., Adiga, A., Hurt, B., Lewis, B., Marathe, M., Porebski, P., Venkatramanan, S., Bartczuk, R., Dreger, F., Gambin, A., Gogolewski, K., Gruziel-S?omka, M., Krupa, B., Moszynski, A., Niedzielewski, K., Nowosielski, J., Radwan, M., Rakowski, F., Semeniuk, M., Szczurek, E., Zieli?ski, J., Kisielewski, J., Pabjan, B., Kheifetz, Y., Kirsten, H., Scholz, M., Biecek, P., Bodych, M., Filinski, M., Idzikowski, R., Krueger, T., Ozanski, T., Bracher, J., and Funk, S.

Abstract

Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast mod

Published
2023
Full Text
View/download PDF

32. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Author

Sherratt, K., Gruson, H., Grah, R., Johnson, H., Niehus, R., Prasse, B., Sandmann, F., Deuschel, J., Wolffram, D., Abbott, S., Ullrich, A., Gibson, G., Ray, E. L., Reich, N. G., Sheldon, D., Wang, Y., Wattanachit, N., Wang, L., Trnka, J., Obozinski, G., Sun, T., Thanou, D., Pottier, L., Krymova, E., Meinke, J. H., Barbarossa, M. V., Leithäuser, N., Mohring, J., Schneider, J., Wlazlo, J., Fuhrmann, J., Lange, B., Rodiah, I., Baccam, P., Gurung, H., Stage, S., Suchoski, B., Budzinski, J., Walraven, R., Villanueva, I., Tucek, V., Smíd, M., Zajícek, M., Pérez Alvarez, C., Reina, B., Bosse, N. I., Meakin, S., Castro, L., Fairchild, G., Michaud, I., Osthus, D., Alaimo Di Loro, P., Maruotti, A., Eclerová, V., Kraus, A., Kraus, D., Pribylova, L., Dimitris, B., Li, M. L., Saksham, S., Dehning, J., Mohr, S., Priesemann, V., Redlarski, G., Bejar, B., Ardenghi, G., Parolini, N., Ziarelli, G., Bock, Wolfgang, Heyder, S., Hotz, T., E. Singh, D., Guzman-Merino, M., Aznarte, J. L., Moriña, D., Alonso, S., Alvarez, E., López, D., Prats, C., Burgard, J. P., Rodloff, A., Zimmermann, T., Kuhlmann, A., Zibert, J., Pennoni, F., Divino, F., Català, M., Lovison, G., Giudici, P., Tarantino, B., Bartolucci, F., Jona Lasinio, G., Mingione, M., Farcomeni, A., Srivastava, A., Montero-Manso, P., Adiga, A., Hurt, B., Lewis, B., Marathe, M., Porebski, P., Venkatramanan, S., Bartczuk, R., Dreger, F., Gambin, A., Gogolewski, K., Gruziel-S?omka, M., Krupa, B., Moszynski, A., Niedzielewski, K., Nowosielski, J., Radwan, M., Rakowski, F., Semeniuk, M., Szczurek, E., Zieli?ski, J., Kisielewski, J., Pabjan, B., Kheifetz, Y., Kirsten, H., Scholz, M., Biecek, P., Bodych, M., Filinski, M., Idzikowski, R., Krueger, T., Ozanski, T., Bracher, J., Funk, S., Sherratt, K., Gruson, H., Grah, R., Johnson, H., Niehus, R., Prasse, B., Sandmann, F., Deuschel, J., Wolffram, D., Abbott, S., Ullrich, A., Gibson, G., Ray, E. L., Reich, N. G., Sheldon, D., Wang, Y., Wattanachit, N., Wang, L., Trnka, J., Obozinski, G., Sun, T., Thanou, D., Pottier, L., Krymova, E., Meinke, J. H., Barbarossa, M. V., Leithäuser, N., Mohring, J., Schneider, J., Wlazlo, J., Fuhrmann, J., Lange, B., Rodiah, I., Baccam, P., Gurung, H., Stage, S., Suchoski, B., Budzinski, J., Walraven, R., Villanueva, I., Tucek, V., Smíd, M., Zajícek, M., Pérez Alvarez, C., Reina, B., Bosse, N. I., Meakin, S., Castro, L., Fairchild, G., Michaud, I., Osthus, D., Alaimo Di Loro, P., Maruotti, A., Eclerová, V., Kraus, A., Kraus, D., Pribylova, L., Dimitris, B., Li, M. L., Saksham, S., Dehning, J., Mohr, S., Priesemann, V., Redlarski, G., Bejar, B., Ardenghi, G., Parolini, N., Ziarelli, G., Bock, Wolfgang, Heyder, S., Hotz, T., E. Singh, D., Guzman-Merino, M., Aznarte, J. L., Moriña, D., Alonso, S., Alvarez, E., López, D., Prats, C., Burgard, J. P., Rodloff, A., Zimmermann, T., Kuhlmann, A., Zibert, J., Pennoni, F., Divino, F., Català, M., Lovison, G., Giudici, P., Tarantino, B., Bartolucci, F., Jona Lasinio, G., Mingione, M., Farcomeni, A., Srivastava, A., Montero-Manso, P., Adiga, A., Hurt, B., Lewis, B., Marathe, M., Porebski, P., Venkatramanan, S., Bartczuk, R., Dreger, F., Gambin, A., Gogolewski, K., Gruziel-S?omka, M., Krupa, B., Moszynski, A., Niedzielewski, K., Nowosielski, J., Radwan, M., Rakowski, F., Semeniuk, M., Szczurek, E., Zieli?ski, J., Kisielewski, J., Pabjan, B., Kheifetz, Y., Kirsten, H., Scholz, M., Biecek, P., Bodych, M., Filinski, M., Idzikowski, R., Krueger, T., Ozanski, T., Bracher, J., and Funk, S.

Abstract

Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast mod

Published
2023
Full Text
View/download PDF

33. Contextual Narratives in Archaeology: Reframing Human History through Sustainability

Author

Bartolucci, Matteo and Bartolucci, Matteo

Abstract

This thesis proposes a contextual approach to the elaboration of narratives in archaeology, suggesting to reframe the history of humanity via parameters of sustainability, negotiated together by experts and their broader public in terms of social and environmental well-being. My suggestion is a shift in perspectives about our past and present, away from the destructive discourses majorly responsible for the current ecosystem crisis. Therefore, the aim is to encourage meaningful alternatives and different paradigms favouring the radical actions required today to deal with this existential threat. To do so, I go through the deconstruction of familiar concepts of ‘progress’ and ‘heritage’ as well as acknowledging the role of archaeology into the making of such dominant narratives, in order to possibly lay the ground for a critically aware stance on true sustainability via the discipline.

Published
2023

34. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Author

Sherratt, K., Gruson, H., Grah, R., Johnson, H., Niehus, R., Prasse, B., Sandmann, F., Deuschel, J., Wolffram, D., Abbott, S., Ullrich, A., Gibson, G., Ray, E. L., Reich, N. G., Sheldon, D., Wang, Y., Wattanachit, N., Wang, L., Trnka, J., Obozinski, G., Sun, T., Thanou, D., Pottier, L., Krymova, E., Meinke, J. H., Barbarossa, M. V., Leithäuser, N., Mohring, J., Schneider, J., Wlazlo, J., Fuhrmann, J., Lange, B., Rodiah, I., Baccam, P., Gurung, H., Stage, S., Suchoski, B., Budzinski, J., Walraven, R., Villanueva, I., Tucek, V., Smíd, M., Zajícek, M., Pérez Alvarez, C., Reina, B., Bosse, N. I., Meakin, S., Castro, L., Fairchild, G., Michaud, I., Osthus, D., Alaimo Di Loro, P., Maruotti, A., Eclerová, V., Kraus, A., Kraus, D., Pribylova, L., Dimitris, B., Li, M. L., Saksham, S., Dehning, J., Mohr, S., Priesemann, V., Redlarski, G., Bejar, B., Ardenghi, G., Parolini, N., Ziarelli, G., Bock, Wolfgang, Heyder, S., Hotz, T., E. Singh, D., Guzman-Merino, M., Aznarte, J. L., Moriña, D., Alonso, S., Alvarez, E., López, D., Prats, C., Burgard, J. P., Rodloff, A., Zimmermann, T., Kuhlmann, A., Zibert, J., Pennoni, F., Divino, F., Català, M., Lovison, G., Giudici, P., Tarantino, B., Bartolucci, F., Jona Lasinio, G., Mingione, M., Farcomeni, A., Srivastava, A., Montero-Manso, P., Adiga, A., Hurt, B., Lewis, B., Marathe, M., Porebski, P., Venkatramanan, S., Bartczuk, R., Dreger, F., Gambin, A., Gogolewski, K., Gruziel-S?omka, M., Krupa, B., Moszynski, A., Niedzielewski, K., Nowosielski, J., Radwan, M., Rakowski, F., Semeniuk, M., Szczurek, E., Zieli?ski, J., Kisielewski, J., Pabjan, B., Kheifetz, Y., Kirsten, H., Scholz, M., Biecek, P., Bodych, M., Filinski, M., Idzikowski, R., Krueger, T., Ozanski, T., Bracher, J., Funk, S., Sherratt, K., Gruson, H., Grah, R., Johnson, H., Niehus, R., Prasse, B., Sandmann, F., Deuschel, J., Wolffram, D., Abbott, S., Ullrich, A., Gibson, G., Ray, E. L., Reich, N. G., Sheldon, D., Wang, Y., Wattanachit, N., Wang, L., Trnka, J., Obozinski, G., Sun, T., Thanou, D., Pottier, L., Krymova, E., Meinke, J. H., Barbarossa, M. V., Leithäuser, N., Mohring, J., Schneider, J., Wlazlo, J., Fuhrmann, J., Lange, B., Rodiah, I., Baccam, P., Gurung, H., Stage, S., Suchoski, B., Budzinski, J., Walraven, R., Villanueva, I., Tucek, V., Smíd, M., Zajícek, M., Pérez Alvarez, C., Reina, B., Bosse, N. I., Meakin, S., Castro, L., Fairchild, G., Michaud, I., Osthus, D., Alaimo Di Loro, P., Maruotti, A., Eclerová, V., Kraus, A., Kraus, D., Pribylova, L., Dimitris, B., Li, M. L., Saksham, S., Dehning, J., Mohr, S., Priesemann, V., Redlarski, G., Bejar, B., Ardenghi, G., Parolini, N., Ziarelli, G., Bock, Wolfgang, Heyder, S., Hotz, T., E. Singh, D., Guzman-Merino, M., Aznarte, J. L., Moriña, D., Alonso, S., Alvarez, E., López, D., Prats, C., Burgard, J. P., Rodloff, A., Zimmermann, T., Kuhlmann, A., Zibert, J., Pennoni, F., Divino, F., Català, M., Lovison, G., Giudici, P., Tarantino, B., Bartolucci, F., Jona Lasinio, G., Mingione, M., Farcomeni, A., Srivastava, A., Montero-Manso, P., Adiga, A., Hurt, B., Lewis, B., Marathe, M., Porebski, P., Venkatramanan, S., Bartczuk, R., Dreger, F., Gambin, A., Gogolewski, K., Gruziel-S?omka, M., Krupa, B., Moszynski, A., Niedzielewski, K., Nowosielski, J., Radwan, M., Rakowski, F., Semeniuk, M., Szczurek, E., Zieli?ski, J., Kisielewski, J., Pabjan, B., Kheifetz, Y., Kirsten, H., Scholz, M., Biecek, P., Bodych, M., Filinski, M., Idzikowski, R., Krueger, T., Ozanski, T., Bracher, J., and Funk, S.

Abstract

Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast mod

Published
2023
Full Text
View/download PDF

35. When nonresponse makes estimates from a census a small area estimation problem: The case of the survey on graduates’ employment status in Italy

Author

Coretto, P, Giordano, G, La Rocca, M, Parrella, ML, Rampichini, C, Ranalli, G, Pennoni, F, Bartolucci, F, Mira, A, Coretto, P, Giordano, G, La Rocca, M, Parrella, ML, Rampichini, C, Ranalli, G, Pennoni, F, Bartolucci, F, and Mira, A

Abstract

In this paper we frame the problem of obtaining estimates from the sur- vey on the employment status of graduates in Italy as a Small Area Estimation problem because of unit nonresponse. We propose to use generalized linear mixed models and to include two variables that can be considered proxies of the response propensity among the set of covariates to make the MAR assumption more tenable. Estimates for degree programmes are obtained as (semi-parametric) empirical best predictions.

Published
2023

36. Discrete latent variable models: Recent and advances and perspectives

Author

Coretto, P, Giordano, G, La Rocca, M, Parrella, ML, Rampichini, C, Bartolucci, F, Greenacre, M, Pandolfi, S, Pennoni, F, Coretto, P, Giordano, G, La Rocca, M, Parrella, ML, Rampichini, C, Bartolucci, F, Greenacre, M, Pandolfi, S, and Pennoni, F

Abstract

After a review of the class of discrete latent variable models in terms of formulation and estimation methods, recent advances and perspectives regarding these models are illustrated. We consider in detail the stochastic block model for social networks and models for spatio-temporal data. Among these developments, we discuss, in particular, the analysis of longitudinal compositional data about expenditures of the Spanish regions over several decades.

Published
2023

37. Contextual Narratives in Archaeology: Reframing Human History through Sustainability

Author

Bartolucci, Matteo and Bartolucci, Matteo

Abstract

This thesis proposes a contextual approach to the elaboration of narratives in archaeology, suggesting to reframe the history of humanity via parameters of sustainability, negotiated together by experts and their broader public in terms of social and environmental well-being. My suggestion is a shift in perspectives about our past and present, away from the destructive discourses majorly responsible for the current ecosystem crisis. Therefore, the aim is to encourage meaningful alternatives and different paradigms favouring the radical actions required today to deal with this existential threat. To do so, I go through the deconstruction of familiar concepts of ‘progress’ and ‘heritage’ as well as acknowledging the role of archaeology into the making of such dominant narratives, in order to possibly lay the ground for a critically aware stance on true sustainability via the discipline.

Published
2023

40. Internal investigations in the trial of corporations in Italy: the practical implication of their admissibility for trial purposes

Author

Bartolucci, Marco Alessandro and Bartolucci, Marco Alessandro

Abstract

The paper conducts a comprehensive analysis of corporate criminal liability in Italy, with a specific focus on internal corporate investigations. After providing a brief overview of the Italian legal system concerning corporate criminal liability, the paper compares internal investigations conducted by companies with the Public Prosecutor's preliminary investigations, examining their respective strengths, weaknesses, and differences. Additionally, the research explores the admissibility of internal investigations during the preliminary investigation stage and in the negotiation process, specifying the inherent limitations and practical challenges in the absence of specific regulations on this matter., O artigo realiza uma análise abrangente da responsabilidade penal corporativa na Itália, com foco específico nas investigações internas corporativas. Após fornecer uma breve visão geral do sistema legal italiano em relação à responsabilidade penal corporativa, o artigo compara as investigações internas conduzidas pelas empresas com as investigações preliminares do Ministério Público, examinando suas respectivas vantagens, fraquezas e diferenças. Além disso, a pesquisa explora a admissibilidade das investigações internas durante a fase de investigação preliminar e no processo de negociação, especificando as limitações intrínsecas e os desafios práticos diante da ausência de regulamentações específicas sobre esse assunto.

Published
2023

42. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Author

Sherratt, K., Gruson, H., Grah, R., Johnson, H., Niehus, R., Prasse, B., Sandmann, F., Deuschel, J., Wolffram, D., Abbott, S., Ullrich, A., Gibson, G., Ray, E. L., Reich, N. G., Sheldon, D., Wang, Y., Wattanachit, N., Wang, L., Trnka, J., Obozinski, G., Sun, T., Thanou, D., Pottier, L., Krymova, E., Meinke, J. H., Barbarossa, M. V., Leithäuser, N., Mohring, J., Schneider, J., Wlazlo, J., Fuhrmann, J., Lange, B., Rodiah, I., Baccam, P., Gurung, H., Stage, S., Suchoski, B., Budzinski, J., Walraven, R., Villanueva, I., Tucek, V., Smíd, M., Zajícek, M., Pérez Alvarez, C., Reina, B., Bosse, N. I., Meakin, S., Castro, L., Fairchild, G., Michaud, I., Osthus, D., Alaimo Di Loro, P., Maruotti, A., Eclerová, V., Kraus, A., Kraus, D., Pribylova, L., Dimitris, B., Li, M. L., Saksham, S., Dehning, J., Mohr, S., Priesemann, V., Redlarski, G., Bejar, B., Ardenghi, G., Parolini, N., Ziarelli, G., Bock, Wolfgang, Heyder, S., Hotz, T., E. Singh, D., Guzman-Merino, M., Aznarte, J. L., Moriña, D., Alonso, S., Alvarez, E., López, D., Prats, C., Burgard, J. P., Rodloff, A., Zimmermann, T., Kuhlmann, A., Zibert, J., Pennoni, F., Divino, F., Català, M., Lovison, G., Giudici, P., Tarantino, B., Bartolucci, F., Jona Lasinio, G., Mingione, M., Farcomeni, A., Srivastava, A., Montero-Manso, P., Adiga, A., Hurt, B., Lewis, B., Marathe, M., Porebski, P., Venkatramanan, S., Bartczuk, R., Dreger, F., Gambin, A., Gogolewski, K., Gruziel-S?omka, M., Krupa, B., Moszynski, A., Niedzielewski, K., Nowosielski, J., Radwan, M., Rakowski, F., Semeniuk, M., Szczurek, E., Zieli?ski, J., Kisielewski, J., Pabjan, B., Kheifetz, Y., Kirsten, H., Scholz, M., Biecek, P., Bodych, M., Filinski, M., Idzikowski, R., Krueger, T., Ozanski, T., Bracher, J., Funk, S., Sherratt, K., Gruson, H., Grah, R., Johnson, H., Niehus, R., Prasse, B., Sandmann, F., Deuschel, J., Wolffram, D., Abbott, S., Ullrich, A., Gibson, G., Ray, E. L., Reich, N. G., Sheldon, D., Wang, Y., Wattanachit, N., Wang, L., Trnka, J., Obozinski, G., Sun, T., Thanou, D., Pottier, L., Krymova, E., Meinke, J. H., Barbarossa, M. V., Leithäuser, N., Mohring, J., Schneider, J., Wlazlo, J., Fuhrmann, J., Lange, B., Rodiah, I., Baccam, P., Gurung, H., Stage, S., Suchoski, B., Budzinski, J., Walraven, R., Villanueva, I., Tucek, V., Smíd, M., Zajícek, M., Pérez Alvarez, C., Reina, B., Bosse, N. I., Meakin, S., Castro, L., Fairchild, G., Michaud, I., Osthus, D., Alaimo Di Loro, P., Maruotti, A., Eclerová, V., Kraus, A., Kraus, D., Pribylova, L., Dimitris, B., Li, M. L., Saksham, S., Dehning, J., Mohr, S., Priesemann, V., Redlarski, G., Bejar, B., Ardenghi, G., Parolini, N., Ziarelli, G., Bock, Wolfgang, Heyder, S., Hotz, T., E. Singh, D., Guzman-Merino, M., Aznarte, J. L., Moriña, D., Alonso, S., Alvarez, E., López, D., Prats, C., Burgard, J. P., Rodloff, A., Zimmermann, T., Kuhlmann, A., Zibert, J., Pennoni, F., Divino, F., Català, M., Lovison, G., Giudici, P., Tarantino, B., Bartolucci, F., Jona Lasinio, G., Mingione, M., Farcomeni, A., Srivastava, A., Montero-Manso, P., Adiga, A., Hurt, B., Lewis, B., Marathe, M., Porebski, P., Venkatramanan, S., Bartczuk, R., Dreger, F., Gambin, A., Gogolewski, K., Gruziel-S?omka, M., Krupa, B., Moszynski, A., Niedzielewski, K., Nowosielski, J., Radwan, M., Rakowski, F., Semeniuk, M., Szczurek, E., Zieli?ski, J., Kisielewski, J., Pabjan, B., Kheifetz, Y., Kirsten, H., Scholz, M., Biecek, P., Bodych, M., Filinski, M., Idzikowski, R., Krueger, T., Ozanski, T., Bracher, J., and Funk, S.

Abstract

Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast mod

Published
2023
Full Text
View/download PDF

43. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Author

Sherratt, K., Gruson, H., Grah, R., Johnson, H., Niehus, R., Prasse, B., Sandmann, F., Deuschel, J., Wolffram, D., Abbott, S., Ullrich, A., Gibson, G., Ray, E. L., Reich, N. G., Sheldon, D., Wang, Y., Wattanachit, N., Wang, L., Trnka, J., Obozinski, G., Sun, T., Thanou, D., Pottier, L., Krymova, E., Meinke, J. H., Barbarossa, M. V., Leithäuser, N., Mohring, J., Schneider, J., Wlazlo, J., Fuhrmann, J., Lange, B., Rodiah, I., Baccam, P., Gurung, H., Stage, S., Suchoski, B., Budzinski, J., Walraven, R., Villanueva, I., Tucek, V., Smíd, M., Zajícek, M., Pérez Alvarez, C., Reina, B., Bosse, N. I., Meakin, S., Castro, L., Fairchild, G., Michaud, I., Osthus, D., Alaimo Di Loro, P., Maruotti, A., Eclerová, V., Kraus, A., Kraus, D., Pribylova, L., Dimitris, B., Li, M. L., Saksham, S., Dehning, J., Mohr, S., Priesemann, V., Redlarski, G., Bejar, B., Ardenghi, G., Parolini, N., Ziarelli, G., Bock, Wolfgang, Heyder, S., Hotz, T., E. Singh, D., Guzman-Merino, M., Aznarte, J. L., Moriña, D., Alonso, S., Alvarez, E., López, D., Prats, C., Burgard, J. P., Rodloff, A., Zimmermann, T., Kuhlmann, A., Zibert, J., Pennoni, F., Divino, F., Català, M., Lovison, G., Giudici, P., Tarantino, B., Bartolucci, F., Jona Lasinio, G., Mingione, M., Farcomeni, A., Srivastava, A., Montero-Manso, P., Adiga, A., Hurt, B., Lewis, B., Marathe, M., Porebski, P., Venkatramanan, S., Bartczuk, R., Dreger, F., Gambin, A., Gogolewski, K., Gruziel-S?omka, M., Krupa, B., Moszynski, A., Niedzielewski, K., Nowosielski, J., Radwan, M., Rakowski, F., Semeniuk, M., Szczurek, E., Zieli?ski, J., Kisielewski, J., Pabjan, B., Kheifetz, Y., Kirsten, H., Scholz, M., Biecek, P., Bodych, M., Filinski, M., Idzikowski, R., Krueger, T., Ozanski, T., Bracher, J., Funk, S., Sherratt, K., Gruson, H., Grah, R., Johnson, H., Niehus, R., Prasse, B., Sandmann, F., Deuschel, J., Wolffram, D., Abbott, S., Ullrich, A., Gibson, G., Ray, E. L., Reich, N. G., Sheldon, D., Wang, Y., Wattanachit, N., Wang, L., Trnka, J., Obozinski, G., Sun, T., Thanou, D., Pottier, L., Krymova, E., Meinke, J. H., Barbarossa, M. V., Leithäuser, N., Mohring, J., Schneider, J., Wlazlo, J., Fuhrmann, J., Lange, B., Rodiah, I., Baccam, P., Gurung, H., Stage, S., Suchoski, B., Budzinski, J., Walraven, R., Villanueva, I., Tucek, V., Smíd, M., Zajícek, M., Pérez Alvarez, C., Reina, B., Bosse, N. I., Meakin, S., Castro, L., Fairchild, G., Michaud, I., Osthus, D., Alaimo Di Loro, P., Maruotti, A., Eclerová, V., Kraus, A., Kraus, D., Pribylova, L., Dimitris, B., Li, M. L., Saksham, S., Dehning, J., Mohr, S., Priesemann, V., Redlarski, G., Bejar, B., Ardenghi, G., Parolini, N., Ziarelli, G., Bock, Wolfgang, Heyder, S., Hotz, T., E. Singh, D., Guzman-Merino, M., Aznarte, J. L., Moriña, D., Alonso, S., Alvarez, E., López, D., Prats, C., Burgard, J. P., Rodloff, A., Zimmermann, T., Kuhlmann, A., Zibert, J., Pennoni, F., Divino, F., Català, M., Lovison, G., Giudici, P., Tarantino, B., Bartolucci, F., Jona Lasinio, G., Mingione, M., Farcomeni, A., Srivastava, A., Montero-Manso, P., Adiga, A., Hurt, B., Lewis, B., Marathe, M., Porebski, P., Venkatramanan, S., Bartczuk, R., Dreger, F., Gambin, A., Gogolewski, K., Gruziel-S?omka, M., Krupa, B., Moszynski, A., Niedzielewski, K., Nowosielski, J., Radwan, M., Rakowski, F., Semeniuk, M., Szczurek, E., Zieli?ski, J., Kisielewski, J., Pabjan, B., Kheifetz, Y., Kirsten, H., Scholz, M., Biecek, P., Bodych, M., Filinski, M., Idzikowski, R., Krueger, T., Ozanski, T., Bracher, J., and Funk, S.

Abstract

Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast mod

Published
2023
Full Text
View/download PDF

44. Proteomics reveals specific biological changes induced by the normothermic machine perfusion of donor kidneys with a significant up-regulation of Latexin

Author

Zaza, G, Neri, F, Bruschi, M, Granata, S, Petretto, A, Bartolucci, M, di Bella, C, Candiano, G, Stallone, G, Gesualdo, L, Furian, L, Zaza, G, Neri, F, Bruschi, M, Granata, S, Petretto, A, Bartolucci, M, di Bella, C, Candiano, G, Stallone, G, Gesualdo, L, and Furian, L

Abstract

Renal normothermic machine perfusion (NMP) is an organ preservation method based on the circulation of a warm (35–37 °C) perfusion solution through the renal vasculature to deliver oxygen and nutrients. However, its biological effects on marginal kidneys are unclear. We therefore used mass spectrometry to determine the proteomic profile of kidney tissue and urine from eight organs reconditioned for 120 min using a Kidney Assist device. Biopsies were taken during the pre-implantation histological evaluation (T-1), at the start of back table preparation (T0), and after 60 and 120 min of perfusion (T60, T120). Urine samples were collected at T0 (urine produced in the first 15 min after the beginning of normothermic reperfusion), T30, T60 and T120. Multiple algorithms, support vector machine learning and partial least squares discriminant analysis were used to select the most discriminative proteins during NMP. Statistical analysis revealed the upregulation of 169 proteins and the downregulation of 196 during NMP. Machine learning algorithms identified the top 50 most discriminative proteins, five of which were concomitantly upregulated (LXN, ETFB, NUDT3, CYCS and UQCRC1) and six downregulated (CFHR3, C1S, CFI, KNG1, SERPINC1 and F9) in the kidney and urine after NMP. Latexin (LXN), an endogenous carboxypeptidase inhibitor, resulted the most-upregulated protein at T120, and this result was confirmed by ELISA. In addition, functional analysis revealed that the most strongly upregulated proteins were involved in the oxidative phosphorylation system and ATP synthesis, whereas the downregulated proteins represented the complement system and coagulation cascade. Our proteomic analysis demonstrated that even brief periods of NMP induce remarkable metabolic and biochemical changes in marginal organs, which supports the use of this promising technique in the clinic.

Published
2023

45. Contextual Narratives in Archaeology: Reframing Human History through Sustainability. Survey dataset

Author

Bartolucci, Matteo and Bartolucci, Matteo

Abstract

The current dataset is associated with the Open Access publication of the Master Degree thesis text “Contextual Narratives in Archaeology: Reframing Human History through Sustainability” (openly accessible at https://doi.org/10.6092/unibo/amsacta/7300). It collects the files related to the online survey carried out for the case study, namely an online questionnaire presented to the cultural association of the village of Canavaccio, meant to preliminary explore the local perception around archaeology as connected to sustainability. The data, here provided in a machine-readable format, are already described in a discursive way in the related publication chapter.

Published
2023

46. Latent potential outcomes: An analysis of the effects of programs aimed at improving student's non-cognitive skills

Author

Bucci, A, Cartone, A, Evangelista, A, Marletta, A, Pennoni, F, Bartolucci, F, Vittadini, G, Vittadini, G., Bucci, A, Cartone, A, Evangelista, A, Marletta, A, Pennoni, F, Bartolucci, F, Vittadini, G, and Vittadini, G.

Abstract

We illustrate a causal latent transition model to evaluate the effects of educational programs administered to pupils in the 6th and 7th grades during their middle school period. The programs are conducted in an Italian region and focus on improving non-cognitive abilities. The interest is in evaluating the effects on the skills acquired in the 8th grade in Italian and Mathematics. The model can be cast in the hidden Markov literature and is formulated as an extension of Rubin's causal model based on potential versions of discrete time-varying latent variables.

Published
2023

47. Analysis of Sacco Hospital longitudinal data by hidden Markov models

Author

Pennoni, F, Bartolucci, F, Spinelli, D, Vittadini, G, Pennoni, F, Bartolucci, F, Spinelli, D, and Vittadini, G

Abstract

We analyze data collected as an observational study by Sacco Hospital patients diagnosed with COVID-19 from the start of the pandemic in early 2020 through December 2022. The list of long-term COVID-19 symptoms includes, among others, fatigue, breathing trouble, rapid heart rhythms, difficulty thinking or concentrating. We analyzed the joint association of the symptoms with the chance of recovering and the effects of covariates through the hidden Markov model for multivariate continuous longitudinal responses that accounts for partially missing outcomes at a given time occasion and dropout.

Published
2023

48. Lung vascular changes as biomarkers of severity in systemic sclerosis-associated interstitial lung disease

Author

Bruni, C., Occhipinti, M., Pienn, M., Camiciottoli, G., Bartolucci, M., Bosello, Silvia Laura, Payer, C., Balint, Z., Larici, Anna Rita, Tottoli, A., Tofani, L., De Lorenzis, E., Lepri, G., Bellando-Randone, S., Spinella, A., Giuggioli, D., Masini, F., Cuomo, G., Lavorini, F., Colagrande, S., Olschewski, H., Matucci-Cerinic, M., Bosello S. L. (ORCID:0000-0002-4837-447X), Larici A. R. (ORCID:0000-0002-1882-6244), Bruni, C., Occhipinti, M., Pienn, M., Camiciottoli, G., Bartolucci, M., Bosello, Silvia Laura, Payer, C., Balint, Z., Larici, Anna Rita, Tottoli, A., Tofani, L., De Lorenzis, E., Lepri, G., Bellando-Randone, S., Spinella, A., Giuggioli, D., Masini, F., Cuomo, G., Lavorini, F., Colagrande, S., Olschewski, H., Matucci-Cerinic, M., Bosello S. L. (ORCID:0000-0002-4837-447X), and Larici A. R. (ORCID:0000-0002-1882-6244)

Abstract

OBJECTIVES: It has recently become possible to assess lung vascular and parenchymal changes quantitatively in thoracic CT images using automated software tools. We investigated the vessel parameters of patients with SSc, quantified by CT imaging, and correlated them with interstitial lung disease (ILD) features. METHODS: SSc patients undergoing standard of care pulmonary function testing and CT evaluation were retrospectively evaluated. CT images were analysed for ILD patterns and total pulmonary vascular volume (PVV) extents with Imbio lung texture analysis. Vascular analysis (volumes, numbers and densities of vessels, separating arteries and veins) was performed with an in-house developed software. A threshold of 5% ILD extent was chosen to define the presence of ILD, and commonly used cut-offs of lung function were adopted. RESULTS: A total of 79 patients [52 women, 40 ILD, mean age 56.2 (s.d. 14.2) years, total ILD extent 9.5 (10.7)%, PVV/lung volume % 2.8%] were enrolled. Vascular parameters for total and separated PVV significantly correlated with functional parameters and ILD pattern extents. SSc-associated ILD (SSc-ILD) patients presented with an increased number and volume of arterial vessels, in particular those between 2 and 4 mm of diameter, and with a higher density of arteries and veins of <6 mm in diameter. Considering radiological and functional criteria concomitantly, as well as the descriptive trends from the longitudinal evaluations, the normalized PVVs, vessel numbers and densities increased progressively with the increase/worsening of ILD extent and functional impairment. CONCLUSION: In SSc patients CT vessel parameters increase in parallel with ILD extent and functional impairment, and may represent a biomarker of SSc-ILD severity.

Published
2023

49. Convolutional Neural Operators for robust and accurate learning of PDEs

Author

Raonić, Bogdan, Molinaro, Roberto, De Ryck, Tim, Rohner, Tobias, Bartolucci, Francesca, Alaifari, Rima, Mishra, Siddhartha, de Bézenac, Emmanuel, Raonić, Bogdan, Molinaro, Roberto, De Ryck, Tim, Rohner, Tobias, Bartolucci, Francesca, Alaifari, Rima, Mishra, Siddhartha, and de Bézenac, Emmanuel

Abstract

Although very successfully used in conventional machine learning, convolution based neural network architectures -- believed to be inconsistent in function space -- have been largely ignored in the context of learning solution operators of PDEs. Here, we present novel adaptations for convolutional neural networks to demonstrate that they are indeed able to process functions as inputs and outputs. The resulting architecture, termed as convolutional neural operators (CNOs), is designed specifically to preserve its underlying continuous nature, even when implemented in a discretized form on a computer. We prove a universality theorem to show that CNOs can approximate operators arising in PDEs to desired accuracy. CNOs are tested on a novel suite of benchmarks, encompassing a diverse set of PDEs with possibly multi-scale solutions and are observed to significantly outperform baselines, paving the way for an alternative framework for robust and accurate operator learning. Our code is publicly available at https://github.com/bogdanraonic3/ConvolutionalNeuralOperator

Published
2023

50. A hidden Markov model for continuous longitudinal data with missing responses and dropout

Author

Pandolfi, S, Bartolucci, F, Pennoni, F, Pandolfi, S, Bartolucci, F, and Pennoni, F

Abstract

We propose a hidden Markov model for multivariate continuous longitudinal responses with covariates that accounts for three different types of missing pattern: (I) partially missing outcomes at a given time occasion, (II) completely missing outcomes at a given time occasion (intermittent pattern), and (III) dropout before the end of the period of observation (monotone pattern). The missing-at-random (MAR) assumption is formulated to deal with the first two types of missingness, while to account for the informative dropout, we rely on an extra absorbing state. Estimation of the model parameters is based on the maximum likelihood method that is implemented by an expectation-maximization (EM) algorithm relying on suitable recursions. The proposal is illustrated by a Monte Carlo simulation study and an application based on historical data on primary biliary cholangitis.

Published
2023

Catalog

Books, media, physical & digital resources
See catalog results